Compact Trial Selection

In an external control trial, participants receiving the intervention being tested are compared with a group of individuals who are separate from the population tested in the trial.

The most common type of external control is a historical control sometimes called a retrospective control Gehan, Individuals receiving the experimental intervention are compared with a group of individuals tested at an earlier time.

For example, the results of a prior clinical trial published in the medical literature may serve as a historical control. The major problem with historical controls is that one cannot ensure that the comparison is fair because of the variability in patient selection and the experimental environment.

If historical controls are obtained from a previous trial conducted in the same environment or by the same investigators, there is a greater chance of reducing the potential bias Pocock, Studies have shown that externally controlled trials tend to overestimate the efficacies of experimental treatments Sacks, Chalmers, and Smith, , although one example has found the treatment effect to be underestimated Farewell and D'Angio, Therefore, when selecting an external control, it is extremely important to try to control for these biases by selecting the control group before testing of the experimental intervention and ensuring that the control group is similar to the experimental group in as many ways as possible.

Trials with external controls sometimes compare the group receiving the experimental intervention with a group tested during the same time period but in another setting. A variation of an externally controlled trial is a baseline-controlled trial e. In a baseline-controlled trial, the health condition of the individuals before they received the experimental intervention is compared with their condition after they have received the intervention.

It is increasingly common for studies to have more than one type of control group, for example, both an active control and a placebo control. In those trials the placebo control serves as an internal control to provide evidence that the active control had an effect.

Some trials compare several doses of a test drug with several doses of an active control drug, all of which may then be compared with a placebo. In some instances, the only practical way to design a clinical trial is as an uncontrolled trial.

Uncontrolled trials are usually used to test new experimental interventions for diseases for which no established, effective treatments are available and the prognosis is universally poor without therapy. In uncontrolled trials, there is no control group for comparison, and it is not possible to use blinding and randomization to minimize bias.

Uncontrolled trials are similar to externally controlled trials, in the sense that the outcomes for research participants receiving the experimental intervention are compared with the outcomes before the availability of the intervention.

Therefore, the scientific grounds for the experimental intervention must be strong enough and its effects must be obvious enough for the positive results of an uncontrolled trial to be accepted. History is replete with examples of failed uncontrolled trials, such as those for the drug laetrile and the anticancer agent interferon Pocock, In many cases investigators may be faced with a situation in which they have a potentially large historical control sample that they want to compare with a small experimental sample in terms of one or more endpoints.

This is typically a problem in observational studies in which the individuals have not been randomized to the control and experimental groups. The question is, how does one control for the bias inherent in the observational nature of these data?

Perhaps the experimental participants have in some way been self-selected for their illness or the intervention that they have received.

This is not a new issue. In fact, it is closely related to statistical thinking and research on analysis of observational data and causal inference. For example, as early as , William G. Cochran considered the use of stratification and subclassification as a tool for removing bias in observational studies.

In a now classic example, Cochran examined the relationship between mortality and smoking using data from a large medical database Cochran, The first row of Table shows that cigarette smoking is unrelated to mortality, but pipe smoking appears to be quite lethal.

The result of this early datamining exercise could have easily misled researchers for some time at the early stages of scientific discovery. It turns out that, at least at the time that these data were collected, pipe smokers were on average much older than cigarette smokers, hence the false association with an increased rate of mortality in the non-stratified group.

Cochran illustrated the effect that stratification i. It might be argued that a good data analyst would never have made this mistake because such an analyst would have tested for relevant interactions with important variables such as age. However, the simple statistical solution to this problem can also be misleading in an analysis of observational data.

For example, nothing in the statistical output alerts the analyst to a potential nonoverlap in the marginal distributions. An investigator may be comparing year-old smokers with year-old nonsmokers, whereas traditional statistical approaches assume that the groups have the same covariate distributions and the statistical analyses are often limited to linear adjustments and extrapolation.

Cochran illustrated that some statistical approaches e. Rosenbaum and Rubin extended the notion of subclassification to the multivariate case i. Propensity score matching allows the matching of cases and controls in terms of their propensities or probabilities of receiving the intervention on the basis of a number of potentially confounding variables.

The result is a matched set of cases and controls that are, in terms of probability, equally likely to have received the treatment. The limitation is that the results from such a comparison will be less generalizable than the results of a randomized study, in which each individual in the total sample has the same likelihood of being a case or a control.

In randomized experiments, ignoring important covariates increases the standard errors of the estimates. By contrast, in observational studies bias can result and the standard errors can be underestimated, leading to an opportunity for a chance association and potentially misleading results.

Such problems become more complex as the number of potential outcome variables increase beyond one. Investigators in clinical trials use the method of masking or blinding , in which neither the participant nor the physician, investigator, or evaluator knows who is assigned to the placebo or control group and who will receive the experimental intervention.

The purpose of masking is to minimize the occurrences of conscious and unconscious biases in the conduct of a clinical trial and in the interpretation of its results Pocock, The knowledge of whether a participant is receiving the intervention under study or is in the control group may have an effect on several aspects of a study, including the recruitment and allocation of participants, their subsequent care, the attitudes of the study participants toward the interventions, the assessment of outcomes, the handling of withdrawals, and the exclusion of data from analysis.

The essential aim of masking is to prevent identification of the interventions that individuals are receiving until all opportunities for biases have passed Pocock, Many randomized trials that have not used appropriate levels of masking show larger treatment effects than blinded studies Day and Altman, In a double-blind trial, neither the participants nor the research or medical staff responsible for the management or clinical evaluation of the individuals knows who is receiving the experimental intervention and who is in the control group.

To achieve this, the interventions being compared during the trial must be disguised so that they cannot be distinguished in any way e. Double-blind trials are thought to produce more objective results, because the expectations of the investigators and participants about the experimental intervention do not affect the outcome of the trial.

Although a double-blind study is ideal for the minimization of bias in clinical trials, use of such a study design may not always be feasible. The interventions may be so different that it is not possible to disguise one from the other, for example, surgery versus drug therapy.

If sham surgery would be necessary to maintain blinding, ethical problems associated with the use of sham surgery may proscribe the use of a double-blind design. Two drugs may have different forms e. One way to design a double-blind trial in this instance is to use a double-dummy technique e.

An alternative design when a double-blind trial is not feasible is the single-blind trial. In a single blind trial the investigators and their colleagues are aware of the intervention but the research participant is not.

When blinding is not feasible, an open-label trial, in which the identity of the intervention is known to both the investigator and the participants, is used. One way to reduce bias in single blind and open-label trials is for those who conduct all clinical assessments to remain blinded to the assignment of interventions.

In single-blind or open-label trials, it is important to place extra emphasis on the minimization of the various known sources of bias as much as possible. Randomization is the process of assigning participants to intervention regimens by using a mechanism of allocation by chance.

Random allocation for the comparison of different interventions has been a mainstay of experimental designs since the pioneering work of Ronald A. Fisher conducted randomized experiments in agriculture in which the experimental units were plots of land to which various crops and fertilizers were assigned in a random arrangement Fisher, Randomization guards against the use of judgment or systematic arrangements that would lead to biased results.

Randomization introduces a deliberate element of chance into the assignment of interventions to participants and therefore is intended to provide a sound statistical basis for the evaluation of the effects of the intervention Pocock, In clinical research, randomization protects against selection bias in treatment assignment and minimizes the differences among groups by optimizing the likelihood of equally distributing people with particular characteristics to the intervention and control arms of a trial.

In randomized experiments, ignoring important covariates, which can lead to differences between the groups, simply increases the standard errors; however, in observational studies, bias can result and the standard errors are underestimated.

There are several different randomization methods Friedman, Furberg, and DeMets, Some of these procedures are designed to ensure balance among intervention groups with respect to important prognostic factors, and thus, the probability of assignment to a particular intervention may change over the course of the trial.

Thus, randomization does not always imply that an individual participant has a 50 percent chance of being assigned to a particular intervention. Clinical trials can use either randomized controls or nonrandomized controls.

In a trial with nonrandomized controls, the choice of intervention group and control group is decided deliberately.

For example, patients with a specific disease characteristic are assigned to the experimental intervention, whereas those with another disease characteristic are assigned to the control arm. On scientific grounds it is easy to conclude that the use of a randomized control group is always preferred.

The consensus view among clinical investigators is that, in general, the use of nonrandomized controls can result in biased and unreliable results Pocock, Randomization in combination with masking helps to avoid possible bias in the selection of participants, their assignment to an intervention or control, and the analysis of their response to the intervention.

The health outcomes assessed are pivotal for both the scientific and substantive credibilities of all trials—and are even more pivotal for small trials.

The selection of outcomes should meet the guidelines for validity Tugwell and Bombardier, In psychology, the concepts of validity and reliability have been developed with the view that measurement is mainly done to discriminate between states and to prognosticate from a single measurement.

For example, an intelligence test can be administered to children at the end of their primary school years to suggest the needed level of secondary education.

In clinical trials, however, measurement of change e. Thus, the concept of responsiveness or sensitivity to change becomes important, but its nomenclature and methodology have not been well developed. In the selection of outcome measures, validity is not the only issue—feasibility also determines which of the valid outcome measures can actually be applied.

The most important criteria for selecting an endpoint include truth, discrimination and feasibility Boers, Brooks, Strand, et al. Any clinical trial design requires precision in the process by which participants are determined to be eligible for inclusion.

The objective is to ensure that participants in a clinical trial are representative of some future class of patients or individuals to whom the trial's findings might be applied Pocock, In the early phases of clinical trial development, research participants are often selected from a small subgroup of the population in which the intervention might eventually be used.

This is done to maximize the chance of observing the specific clinical effects of interest. In these early stages it is sometimes necessary to compromise and study a somewhat less representative group Pocock, Similarly, preliminary data collected from one population e.

A standard approach asks five questions:. How small a treatment difference is it important to detect, and with what degree of certainty should that treatment difference be demonstrated?

Statistical methods can then be developed around qualitative or quantitative outcomes. A critical aspect of trial design is to first make use of statistical methods to determine the population size needed to determine the feasibility of the clinical trial.

The number of participants in a clinical trial should always be large enough to provide a sufficiently precise answer to the question posed, but it should also be the minimum necessary to achieve this aim. A trial with only a small number of participants carries a considerable risk of failing to demonstrate a treatment difference when one is really present Type II error see the Glossary for explanations of Type I and Type II errors.

In general, small studies are more prone to variability and thus are likely to be able to detect only large intervention effects with adequate statistical power.

Variance is a measure of the dispersion or variation of data within a population distribution. In the example of the effects of microgravity on bone mineral density loss during space travel see Box , there is a tendency to assume that the astronaut is the unit of analysis and hence to focus on components of variance across astronauts.

In this case, it becomes important to consider the other components of variance in addition to the among-person variance. In a study of bone mineral density loss among astronauts, the components of variance may include:.

variation in bone mineral density across time for a single astronaut on Earth or in microgravity;. differences in bone mineral density for that astronaut on Earth and after a fixed period of time in microgravity; and.

differences in bone mineral density among astronauts both on Earth and in microgravity. The goal would be to characterize changes for an individual astronaut or a small group of astronauts, even though they do not perfectly represent a large population.

It is reasonable to focus on true trends for a particular astronaut over time, which requires careful repeated measurements over time and which makes relevant the component of variance within a person rather than the component of variance among persons.

Significance tests e. However, statistical significance is not the same as clinical or societal significance. Clinical or societal significance relevance must be assessed in terms of whether the magnitude of the observed effect is meaningful in the context of established clinical practice or public health.

An increase of risk from 1 in 10 to 2 in 10 has a clinical implication different from that of an increase of 1 in 10, to 2 in 10,, even though the risk has doubled in each case.

In hypothesis testing, the null hypothesis and one's confidence in either its validation or refute are the issue:. The basic overall principle is that the researcher's theory is considered false until demonstrated beyond reasonable doubt to be true This is expressed as an assumption that the null hypothesis, the contradiction of the researcher's theory, is true A statistical test defines a rule that, when applied to the data, determines whether the null hypothesis can be rejected Both the significance level and the power of the test are derived by calculating with what probability a positive verdict would be obtained the null hypothesis rejected if the same trial were run over and over again Kraemer and Thiemann, , pp.

A clinical trial is often formulated as a hypothesis as to whether an experimental therapy is effective. However, confidence intervals may provide a better indication of the level of uncertainty. In the clinical trial setting, the hypothesis test is natural, because the goal is to determine whether an experimental therapy should be used.

In clinical trials, confidence intervals are used in the same manner as hypothesis tests. Thus, if the interval includes the null hypothesis, one concludes that the experimental therapy has not proved to be more effective than the control. To obtain the significance level, hypothetical repeats of trials are done when the null hypothesis is taken to be true.

To obtain power, repeat tests are done when the alternative hypothesis is correct. To compute power, the researcher must have developed from preliminary data a critical-effect size, that is, a measure of how strong the theory must minimally be to be important to the individual being offered the therapy or important to society Kraemer and Thiemann, , p.

Changing designs or measures used or choosing one valid test over another changes the definition of effect size. Moreover, the critical-effect size is individual- or population-specific as well as measurement-specific Kraemer and Thiemann, Modern clinical trials go back more than 40 years, and a wide variety of clinical trial designs have been developed and adapted over the past 25 years.

To the extent possible, each of these designs uses the concepts of control and randomization to make comparisons among groups Box Some of these designs, which are generally used in larger studies, can also be adapted for use in some small studies. For example, crossover designs can be used in small clinical studies and can be used in within-subject trials.

Each is described below. Traditional Designs for Clinical Trials. Parallel-group design Crossover design. The most common clinical trial design is the parallel-group design, in which participants are randomized to one of two or more arms Pocock, These arms include the new intervention under investigation and one or more control arms, such as a placebo control or an active control.

The randomized parallel-group design is typically used to evaluate differences in the effects of different interventions across time. Trials that use the parallel-group design are often double blinded.

Because of the improved ability to control for bias through randomization and blinding, the analysis of such trials and the interpretation of their results are generally straightforward.

The crossover design compares two or more interventions by randomly assigning each participant to receive the interventions being tested in a different sequence.

Once one intervention is completed, participants are switched to another intervention. For example, in a two-by-two crossover design, each participant randomly receives one drug for one period of time and then another drug for a second period of time, with the administration of each drug separated by a washout period i.

With this type of study, each participant serves as his or her own control. There are several advantages to this trial design, including a reduction in the number of participants required to achieve a statistically significant result and the ability to control for patient specific effects.

This design can also be useful for studying a patient's response to short periods of therapy, particularly for chronic conditions in which the initial evaluation of treatment efficacy is concerned with the measurement of short-term relief of symptoms Pocock, A criticism of this design is that the effects of one intervention may carry over into the period when the next intervention is given.

Crossover studies cannot be done if the effects of the interventions are irreversible e. Additional problems with crossover studies occur if participants withdraw from the study before they receive both interventions or the outcomes are affected by the order in which the interventions are administered Senn, Crossover designs are occasionally used in psychological studies because of the opportunity to use each patient at least twice and because of the probability that the component of the variance within individual patients is smaller than between patients Matthews, In a factorial design, two or more treatments are evaluated simultaneously with the same participant population through the use of various combinations of the treatments.

For example, in a two-by-two factorial design, participants are randomly allocated to one of the four possible combinations of two treatments, treatments A and B: treatment A alone, treatment B alone, both treatments A and B, or neither treatment A nor treatment B.

The usual intention of using this design is to make efficient use of clinical trial participants by evaluating the efficacies of the two treatments with the same number of participants that would be required to evaluate the efficacy of either one alone. The success of this approach depends on the absence of any relevant interaction between treatments A and B so that the effect of treatment A is virtually identical whether or not treatment B is administered.

This design can also be used to test the interaction of treatments A and B, but then, the advantages of efficiency no longer apply because much larger trials are necessary to detect a clinically relevant interaction. The factorial design can also be used to establish the dose-response characteristics of a combination product, for example, one that combines treatments C and D.

Different doses of treatment C are selected, usually including a dose of zero placebo , and similar doses of treatment D are also chosen. Participants in each arm of the trial receive a different combination of doses of treatments C and D.

The resulting estimate of the response may then be used to help to identify an appropriate combination of doses of treatments C and D for clinical use. In an add-on design, a placebo-controlled trial of an experimental intervention is tested with people already receiving an established, effective treatment.

Thus, all participants receive the established, effective treatment. The add-on design is especially useful for the testing of experimental interventions that have a mechanism of action different from that of the established, effective treatment. Experimental interventions for patients with acute myocardial infarctions and, increasingly, patients with rheumatoid arthritis, for example, are often tested in studies with this design.

The add-on design is the only one that can be used in long-term studies of treatments for heart failure since standard therapy is lifesaving and cannot be denied Temple, However, the add-on design is most useful for the testing of experimental interventions that have mechanisms of action different from that of the established, effective treatment.

In a randomized withdrawal design, individuals who respond positively to an experimental intervention are randomized to continue receiving that intervention or to receive a placebo. This trial design minimizes the amount of time that individuals receive a placebo Temple, During the trial, the return of symptoms or the ability to continue participation in the trial are study endpoints Temple, The advantages of this study design are that individuals receiving the experimental intervention continue to do so only if they respond, whereas individuals receiving the placebo do so only until their symptoms return.

Disadvantages include carryover effects, difficulties assessing whether the underlying disease process is still active, and long lag times to adverse events if the disease is in remission.

This design is more appropriate in phase I and II trials involving healthy volunteers because it is less likely that effective treatments are being withdrawn from those who need it.

In some studies, however, measurement of the placebo effect is essential e. In those cases, voluntary, informed consent is essential, as is the provision of care during the withdrawal period. The early-escape design is another way to minimize an individual's duration of exposure to a placebo.

In the early-escape design, participants are removed from the study if symptoms reach a defined level or they fail to respond to a defined extent.

The failure rate can then be used as the measure of efficacy. Thus, in a study with an early-escape design, participants are only briefly exposed to ineffective interventions Temple, Multicenter trials, although not a traditional design, provide an efficient way of establishing the efficacy of a new intervention; however, certain caveats must be noted.

Sometimes multicenter trials provide the only means of accruing a sample of sufficient size within a reasonable time frame. Another advantage of multicenter trials is that they provide a better basis for the subsequent generalization of findings because the participants are recruited from a wider population and the treatment is administered in a broader range of clinical settings.

In this sense, the environment in which a multicenter trial is conducted might more truly represent the environment for future uses of the test intervention. On the other hand, multicenter trials may require the use of multiple standards and quality control. A number of trial designs especially lend themselves to studies with small numbers of participants, including single subject n -of-1 designs, sequential designs, decision analysis-based designs, ranking and selection designs, adaptive designs, and risk-based allocation designs Box Special Design Issues for Small Clinical Trials.

n -of-1 design Sequential design. Clinicians are often faced with treatment decisions when they cannot rely on the results of an RCT because the results do not apply to that patient or a relevant trial might not yet have been done.

Trials with this type of design referred to as a trial with an n -of-1 design have a long tradition in the behavioral sciences and have more recently been used in clinical medicine Johannessen, Trials with such designs can improve the certainty of a treatment decision for a single patient; a series of trials with such designs may permit more general inferences to be drawn about a specific treatment approach Johannessen, They also become useful when a population is believed to be heterogeneous.

The central premise of trials with such designs is that the patient e. The factors that can mislead physicians conducting conventional therapeutic trials—the placebo effect, the natural history of the illness, and expectations about the treatment effect—can be avoided in trials of therapy with n -of 1-designs by safeguards that permit the natural, untreated course of the disorder to be observed and by keeping the patient and the clinician blind to the timing of active treatment.

Guyatt and colleagues describe one method of conducting an RCT with an n -of-1 design:. RCTs with n -of 1 designs may be indicated if an RCT has shown that some patients are unresponsive to treatment, if there is doubt about whether a treatment is really providing a benefit to a particular patient; when the patient insists on taking a treatment that the clinician thinks is useless or potentially harmful, when a patient is experiencing symptoms suspected to be medication side effects but neither the patient nor the clinician is certain, and when neither the clinician nor the patient is confident of the optimal dose of a medication or replacement therapy Edgington, In addition, RCTs with n -of-1 designs are most useful for the study of treatments for chronic conditions for which maintenance therapy is likely to be continued for long periods of time and if the treatment effect occurs soon after the initiation of treatment and ceases soon after the withdrawal of treatment.

Trials with n -of 1 designs are also attractive for the study of vaguely defined or heterogeneous conditions Table For patients with these conditions, studies with n -of-1 designs may generate new hypotheses for the design of subsequent conventional group trials and can bridge the gap between research and clinical practice Johannessen, Considerations in Performing a Trial with an n -of-1 Design.

One concern about trials with n -of-1 designs is whether clinically relevant targets of treatment can be measured. Outcome measures often extend beyond a set of physical signs e. Thus, in most situations it is preferable to directly measure a patient's symptoms, well being, or quality of life.

The measurement of a patient's symptoms may also include the side effects of treatment Guyatt, Sackett, Adachi, et al.

One of the advantages to not specifying the number of pairs of treatment periods in advance is that the trial can be stopped at any time. If, on the other hand, one wishes to conduct a standard statistical analysis of data e.

Regardless of whether the number of treatment periods is specified in advance, it is advisable to have at least two pairs of treatment periods before breaking the trial Guyatt, Conclusions drawn after a single pair of treatments are likely to be either false positive that the treatment is effective when it is not or false negative that the treatment is not effective when it is.

Moreover, a positive effect of treatment in one patient is not a reliable predictor of the responses in future patients.

A preliminary treatment period with active therapy, during which both the clinician and the patient know that active therapy is being received, could save time.

If there is no evidence of a response during such an open trial or if intolerable side effects occur, an RCT with an n -of-1 design may be meaningless or impossible. An open preliminary treatment period may also be used to determine the optimal dose of the medication to be used in the trial.

If requirements similar to those required for conventional group trials—strict entry criteria, uniform treatment procedures, consensus scales for outcome measures, and acceptable statistical tests—are applied to a series of trials with n -of-1 designs, conclusions may be generalizable to the target population Johannessen, ; Zucker, Schmid, McIntosh, et al.

This has the advantage that the patients are exposed to placebo only for as long as is needed to get an answer both for the patients and for the main population database.

A repeated-measures design is likely to be very useful in small studies. The extreme of a small repeated-measures design is the study with an n -of-1 design.

At the design phase of a study with a repeated-measures design, the correlation structure of the measures is an important parameter. One would need to explore the feasibility i. In a study with a sequential design, participants are sequentially enrolled in the study and are assigned a treatment assignment is usually at random.

The investigator then changes the probabilities that participants will be assigned to any particular treatment on the basis of as they become available.

The object is to improve the efficiency, safety, or efficacy of the experiment as it is in progress by changing the rules by which one determines how participants are allocated to the various treatments. Strategies for sequential dose-response designs include up-and-down methods, stochastic approximation methods, maximum-likelihood methods, and Bayesian methods.

Recently, attention has been focused on the continual reassessment methods which is a Bayesian sequential design Durham, Flournoy, and Rosenberger, Random-walk rules are particularly attractive for use in the design of dose-response studies for several reasons: exact finite and asymptotic distribution theory is completely worked out, which allows the experimenter to choose design parameters for the most ethical allocation scheme; specific designs can be chosen that allow the chosen design points to be distributed unimodally around a quantile of interest; the designs are very simple to implement; and the designs operate on a finite lattice of dosages Durham, Flournoy, and Rosenberger, Random-walk rules identify a class of rules for which the sample paths form random walks.

Thus, if there is a fixed probability of transitioning from state A to state B and another fixed probability of transitioning from state B to state A in a two-state process a Markov chain , then sequences of states such as A, B, B, A, B, are random walks.

The design allocates treatments to pairs of participants in a way that causes the treatment distribution to cluster around the treatment with a maximum probability of success Dixon and Mood, ; Kpamegan and Flournoy, An up-and-down design has some advantages in clinical trials, in that it allows more conservative movement across a range of treatments.

To optimize an up-and-down design, one treats individuals in pairs, with one receiving the lower-dose treatment and the other receiving the higher-dose treatment. If the lower-dose treatment results in a treatment failure and the higher-dose treatment results in a treatment success, the doses of the treatment are increased for the next pair.

Conversely, if the patient with the lower-dose treatment has a treatment success and the patient with the higher-dose treatment has a treatment failure, then the doses of the treatment are decreased for the next pair.

In this simple model, if there are two treatment successes or two treatment failures, the study is stopped. This design allows early estimations of the effective dosage range to be obtained before investigators proceed with large-scale randomized trials Flournoy, in press.

Sequential group designs are useful for the monitoring and accumulation of study data, while they preserve the Type I error probability at a desired significance level, despite the repeated application of significance tests Kim and DeMets, Parallel-groups are studied until a clear benefit is seen or it is determined that no difference in treatments exists Lai, Levin, Robbins, et al.

The sequential group design allows results to be monitored at specific time intervals throughout the trial so that the trial may be stopped early if there is clear evidence of efficacy.

Safety monitoring can also be done, and trials can be stopped early if unacceptable adverse effects occur or if it is determined that the chance of showing a clinically valuable benefit is futile. Because there is a need in all clinical trials—as dictated by ethical requirements—to assess results throughout the course of the trial, there is a potential that the blind will be broken, depending on how the results are assessed and by whom.

The disadvantage of this approach is that in most trials patients are heterogeneous with respect to the important prognostic factors, and these methods do not protect against the introduction of bias as a result of changes in the types of patients entering into a clinical trial over time.

Moreover, for patients with chronic diseases, responses are usually delayed so long that the advantages of this approach are often lost. Decision analysis Pauker, can be informative in the experimental design process.

Modeling of a clinical situation a priori allows testing of variables, which allows determination of the potential impact of each variable on the decision. Framing the question starts the decision analysis-based design process. One explicitly considers both decision e.

A utility is assigned to each outcome. Utilities have numeric values, usually between 0 and 1, that reflect the desirability of an outcome; that is, they incorporate the weighting of the severity or importance of the possible adverse outcomes as well as the weighting of the severity or importance of the beneficial outcomes Drummond, O'Brien, Stoddart, et al.

Decision analysis combines the probability of each outcome with the utility to calculate an expected utility for each decision. During the planning phase for a study, decision analysis is used to structure the question. One obtains either from data or from expert opinion best estimates for each probability and utility.

One then varies potential important values either probability or utility over a likely range. Thus, decision analysis can direct small trials to focus on these important variables.

The integrity of an analysis depends both on the values and on the model's structure. One should make both values and structure available for evaluation. One can use the process of varying the value assumptions known as sensitivity analysis to determine if a value's precision would change one's decision.

It is important to recognize, however, that decision analysis is dependent on the assumptions made about parameter values and model structure. Reviews of decision analyses should include careful critique of the model structure. See Chapter 3 for a further discussion and an example of decision analysis.

Selection problems pervade the conduct of clinical trials. Statisticians can provide rational procedures for selection of the best of several alternatives. The formulation of the goal for the statistical significance of a trial influences sample size in a substantial way.

The hypothesis test has been the predominant formulation used in the design of large-scale, randomized trials, but other paradigms deserve careful consideration, especially in situations with small sample sizes. One such paradigm is ranking and selection.

Ranking and selection procedures are statistical techniques for comparison of the parameters for multiple study k populations under the assumption that these parameters are not all the same Gibbons, Olkin, and Sobel, The methods, known generally as ranking and selection procedures, include techniques appropriate for the achievement of many different goals, although a careful formulation of the corresponding problem is needed for each goal.

Suppose there are k populations and that each population is characterized by a parameter. For example, the k populations are normally distributed with different means and a common variance.

In this context, populations for which mean values are large are preferable to populations for which mean values are small. For any given set of k populations, some of the goals that can be accomplished by these methods are. selection of a random number of populations such that all populations better than a control population or a standard are included in the selected subset;.

selection of a random number of populations, say r, which includes the t best populations;. selection of a fixed number of populations, say r, which includes the t best populations;.

ordering of all the k populations from best to worst or vice versa ; or. ordering of a fixed-size subset of the populations from best to worst or vice versa Gibbons, Olkin, and Sobel, Ranking and selection procedures are particularly appropriate for answering questions such as the following Gibbons, Olkin, and Sobel, :.

Instead of formulating the goal of a trial as the definitive rejection of a null hypothesis when it is false with a high degree of statistical power while limiting its rejection when it is true at a given level of a Type I error rate in planning a selection trial a clinician might reason as explained in Box Example of a Selection Trial.

Over the course of the coming year, a clinician will have N patients to treat with disease D. The clinician can treat these patients with therapy A or therapy B, but it is unclear which therapy is better.

One thing is clear, more A related goal is to rank three or more treatments in order of preference. Methods for ranking and selection lend themselves naturally to sequentialization. Sequential selection procedures can further reduce the sample size required to select the best of two or more treatments Levin and Robbins, One of the ways in which ranking and selection methods can be of help in a process is by ruling out poor competitors.

Suppose that investigators must choose the best of five interventions. With small sample sizes the investigators may not be able to choose the best but might be able to assert that the best is among a group of three of the interventions, although they are not sure which one is the best.

Subsequent studies can then focus on choosing the best of the three interventions. Even more intriguing criteria have been proposed for the selection of a superior treatment. Consider again the finite patient horizon of N patients to be treated over the course of a given time period.

Suppose n pairs of patients for a total of 2 n patients are to be considered in the trial phase, with treatment A or treatment B randomly allocated within pairs. The ethical cost function is the total number of patients given the truly inferior treatment multiplied by the magnitude of the treatment efficacy difference.

It is simple to implement a sequential version of the trial phase; it also has the virtue of achieving a substantially lower average ethical cost than that which can be achieved with a fixed sample size in the trial phase.

A surprising feature of a large class of reasonable sequential stopping rules for the trial phase is that they can reduce the average ethical cost for a fixed sample size, even when the ethical cost is optimized for a given value of AD. For example, one such rule will reach a decision in the trial phase in which n is no more than one-sixth of N.

The main point for consideration in small trials, however, is that it may not be obvious how one rationalizes the trade-off between the number of patients put at risk in the trial and an ultimately arbitrary Type I error rate in a conventional trial.

On the other hand, it may be much more desirable to design a selection trial with an ethical cost function that directly incorporates the number of patients given inferior treatment. Adaptive designs have been suggested as a way to overcome the ethical dilemmas that arise when the early results from an RCT clearly begin to favor one intervention over another.

An adaptive design seeks to skew assignment probabilities to favor the better-performing treatment in a trial that is under way Rosenberger, Adaptive designs are attractive to mathematicians and statisticians because they impose dependencies that require the full arsenal of techniques and stochastic processes Rosenberger, An assortment of adaptive designs has been developed over the past few decades, including a variety of urn models that govern the sampling mechanism.

Adaptive design can be associated with complex analytical problems. If the sample size is small enough, an exact analysis by exhaustive enumeration of all sample paths is one way to provide an answer. If the sample size is larger but still not large, a Monte Carlo simulation can provide an accurate analysis.

If the sample size is large, then standard likelihood-based methods can be used. An example of an adaptive design is described in Box Play-the-Winner Rule as an Example of Adaptive Design. A simple version of a randomized version of the play-the-winner rule follows.

An urn contains two balls; one is labeled A and the other is labeled B. When a patient is available for treatment assignment, more A major advantage of adaptive design is that over time more patients will be assigned to the more successful treatment.

Stopping rules and data analysis for these types of designs are complicated Hoel, Sobel, and Weiss, , and more research is needed in this area. As with sequential designs, the disadvantage of adaptive designs is that in most trials, patients are heterogeneous with respect to the important prognostic factors, and these methods do not protect against bias introduced by changes in the types of patients entering into a trial over time.

Morever, for patients with chronic diseases, responses are usually delayed so long that the advantages of this approach are often lost. Also, multiple endpoints are usually of interest, and therefore, the entire allocation process should not be based on a single response.

Play-the-winner rules can be useful in certain specialized medical situations in which ethical challenges are strong and one can be reasonably certain that time trends and patient heterogeneity are unimportant.

These rules can be especially beneficial when response times are short compared with the times between patient entries into a study. An example of this is the development of extracorporeal membrane oxygenation Truog, ; Ware, Risk-based allocation, a nonrandomized design, has a very specific purpose: to allow individuals at higher risk or with greater disease severity to benefit from a potentially superior experimental treatment.

Because the design is nonrandomized, its use should be considered only in situations in which an RCT would not be possible. For example, when a therapy is readily available outside the study protocol or when a treatment has been in use for a long time and is perceived to be efficacious, even though it has never been subjected to a randomized trial, a nonrandomized risk-based allocation approach may be useful.

Bone marrow transplantation for the treatment of advanced breast disease is an illustration. For each patient, baseline patient and tumor characteristics, treatment regimen s , time on treatment s and survival were retrieved from medical records and updated every three months. Therapeutic clinical trial enrollment was evaluated from the date of reporting molecular profiling results until 9 January Decisions about trial enrollment were based upon trial availability, patient or physician preference, and did not follow a pre-specified algorithm.

Targeted lesion measurements and RECIST 1. Descriptive statistics were used to summarize patient characteristics, profiling results, and anti-tumor activity. Comparisons between patients with profiling results treated on genotype-matched and genotype-unmatched trials were performed using a generalized estimating equation GEE model [ 7 ].

A multi-variable GEE model for response included trial matching by genotype, gender, trial phase, number of lines of prior systemic therapy, investigational agent class, age, tumor type, and sequencing platform. A mixed model was used to compare time on treatment, defined as the date of trial enrollment until the date of discontinuation of investigational treatment.

A robust score test was used to compare overall survival following trial enrolment between genotype-matched and genotype-unmatched groups [ 8 ].

These comparisons accounted for individual patients who were included on multiple therapeutic trials [ 8 ]. The median follow-up from reporting results was 18 months range, 1—33 months. Median laboratory turnaround time sample receipt to report was 32 days range, 6— days.

We attribute the difference in mutation landscape between these two platforms to inclusion of TP53 in the TSACP assay but not in MALDI-TOF see Additional file 1 : Supplemental Methods.

Mutation frequency was calculated as number of variant occurrences within each gene divided by the total number of patients. Class 1 and 2 variants are the most clinically significant with known actionability for the specific variant in the tumor site tested Class 1 or a different tumor site Class 2 [ 4 ].

Distribution of patients by tumor site and most actionable variant identified [ 4 ]. a Proportion and number of variants by tumor site, TSACP. b Actionability of variants by tumor site, TSACP. c Proportion and number of variants by tumor site, MALDI-TOF.

d Actionability of variants per case by tumor site, MALDI-TOF. Patients with more than one variant were counted once by their most actionable variant class.

Total number of patients is indicated by value within or below each bar section. Patients with pancreatobiliary, upper aerodigestive tract, and other solid tumors were least likely to be treated on genotype-matched trials. A complete list of genotype-matched clinical trials by drug class, somatic genotype variant level , and tumor type are summarized in Table 3.

The age and sex distribution, as well as the number of lines of prior systemic therapy, were similar between the genotype-matched and genotype-unmatched trial patient cohorts Table 2. Genotype-matched trial patients were more likely to be treated with targeted drug combinations without chemotherapy or immunotherapy.

Two patients were identified with TP53 variants in DNA extracted from blood. The first patient was a year-old woman diagnosed with metastatic breast cancer, with a prior papillary thyroid cancer at the age of 28 years, who had a heterozygous germline TP53 c.

ArgCys pathogenic mutation. Her family history was notable for her mother who died from cancer of unknown primary at the age of 63 years and a maternal aunt with breast cancer at the age of 62 years.

The second patient, a year-old woman diagnosed with metastatic cholangiocarcinoma, had no family history of malignancy.

We detected a heterozygous TP53 c. This finding is not consistent with inherited Li-Fraumeni syndrome LFS , but may represent either clonal mosaicism or an age-related or treatment-related mutation limited to blood. We demonstrated that molecular profiling with mass-spectrometry-based genotyping or targeted NGS can be implemented in a large academic cancer center to identify patients with advanced solid tumors who are candidates for genotype-matched clinical trials.

The rapid enrolment to our study reflects the high level of motivation of patients and their oncologists to pursue genomic testing that has been previously reported by our group [ 9 , 10 ] and others [ 1 , 11 — 13 ].

To facilitate trial accrual, we incorporated multidisciplinary tumor board discussions, physician-directed email alerts with genotype-matched trial listings available at our institution, and individual physician summaries of profiling results. In spite of these efforts, the rate of genotype-matched clinical trial enrolment was low, due to patient deterioration, lack of available clinical trials, and unwillingness of patients to travel for clinical trial participation.

There was no difference in proportion of patients treated on genotype-matched trials who underwent profiling using MALDI-TOF or a larger targeted NGS panel. A key finding of our study is that patients in genotype-matched trials were more likely to achieve response than patients in genotype-unmatched trials.

Albeit a non-randomized comparison, this finding comprises an important metric and distinguishes our molecular profiling program from other prospective studies that have not tracked longitudinal clinical outcome [ 1 , 16 , 17 ].

This study was performed prior to the era of multiplex mutation testing and many patients received MP-guided therapy with cytotoxic therapy using biomarker data that has not been shown to influence treatment response. The same investigators from MD Anderson recently reported the results of their prospective genomic profiling study that enrolled patients with advanced refractory solid tumors assessed in their phase I program [ 20 ].

Since ER and HER2 testing are routinely performed in breast cancer patients to guide standard therapies, these patients would not have been included in our matched therapy cohort if the ER and HER2 status were known prior to enrollment in our molecular profiling study. The only randomized trial that has prospectively assessed the utility of molecular profiling SHIVA reported no difference in objective response or PFS for patients treated with genotype-matched versus standard treatments [ 13 ].

Patients were matched to a limited range of approved targeted agents following a predefined algorithm that did not include best-in-class investigational agents that are being tested in early phase clinical trials. Despite the negative results of SHIVA, enthusiasm to conduct genomic-based clinical trials such as NCI-MATCH [ 12 ] [NCT], and LUNG-MAP [ 22 ] [NCT] remains strong to further define the value of precision medicine.

The findings of our study, in which the majority of patients treated on genotype-matched trials were enrolled in phase I targeted therapy trials, are consistent with a recent meta-analysis of phase I trials that demonstrated a higher overall response rate Measuring the clinical utility of molecular profiling is difficult [ 3 ].

We did not comprehensively capture how testing results influenced clinical decisions outside of therapeutic clinical trial enrolment, such as reclassification of tumor subtype and site of primary based on mutation results.

For example, we enrolled a patient with an unknown primary cancer with intra-abdominal metastases that was found to harbor a somatic IDH1 p. ArgCys variant, leading to the reclassification as a likely intrahepatic cholangiocarcinoma.

We also did not fully evaluate the use of testing results to avoid ineffective standard treatments i. KRAS exon 4 somatic variants in colorectal cancer to inform decision not to use EGFR monoclonal antibody treatment and treatment with approved targeted agents outside of their approved indications.

Few patients in our study received targeted treatments based upon profiling results outside of clinical trials, due to limited access to targeted drugs outside of publicly funded standard-of-care indications in Ontario. New technological advances are being studied in molecular profiling programs—including larger gene panels [ 2 , 17 ]; whole exome [ 16 ], whole genome WGS or RNA sequencing RNA-Seq [ 24 , 25 ]; and integrative systems biology analyses of deregulated cellular pathways [ 26 ].

Greater access to clinical trials for genomically characterized patients, such as umbrella and basket trial designs [ 27 ], may also improve the success of genotype-treatment matching. To assess whether decision support tools integrated at the point of care can improve enrollment of patients on genotype-matched trials, we are piloting a smart phone application to help physicians identify genotype-matched trials for their patients with profiling data.

There are several limitations of our study. Only a single archival sample was profiled for each patient, often obtained many years prior to molecular testing. Fresh biopsy of a current metastatic lesion for molecular profiling at the time of study enrolment may have yielded different results due to clonal evolution or tumor heterogeneity [ 28 ].

Our genomic testing was limited to hotspot point mutation testing or limited targeted sequencing and did not include gene copy number alterations or recurrent translocations that may be important for the selection of genotype-matched therapy.

Our study population also included many patients with heavily pre-treated metastatic disease who were not well enough for further therapy when results of molecular testing were reported. In addition, tumor response is an imperfect surrogate endpoint to assess therapeutic benefit in early phase clinical trials that should interpreted with caution [ 28 ].

We did not observe a difference in time on treatment or overall survival for patients treated on genotype-matched versus genotype-unmatched clinical trials. PFS data were not available in our cohort precluding a comparison of the outcome of genotype-matched therapy with the immediate prior line of treatment, as has been reported by other investigators [ 13 , 14 , 21 ].

We provide preliminary evidence that genotype-matched trial treatment selected on the basis of molecular profiling was associated with increased tumor shrinkage, although only a small proportion of profiled patients benefitted from this approach.

Through this initiative, we have created a valuable repository of data and tumor samples that are amenable to additional research and data sharing initiatives. Greater efforts should be made to expand opportunities for genotype-trial matching and further studies are needed to evaluate the clinical utility of targeted NGS profiling.

Meric-Bernstam F, Brusco L, Shaw K, Horombe C, Kopetz S, Davies MA, et al. Feasibility of large-scale genomic testing to facilitate enrollment onto genomically matched clinical trials.

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Blanchette PS, Spreafico A, Miller FA, Chan K, Bytautas J, Kang S, et al. Genomic testing in cancer: Patient knowledge, attitudes, and expectations.

André F, Bachelot T, Commo F, Campone M, Arnedos M, Dieras V, et al. Lancet Oncol. Conley BA, Doroshow JH. Molecular analysis for therapy choice: NCI MATCH.

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Von Hoff DD, Stephenson JJ, Rosen P, Loesch DM, Borad MJ, Anthony S, et al. Article Google Scholar. Lessons learned from the application of whole-genome analysis to the treatment of patients with advanced cancers.

Mol Case Stud. Van Allen EM, Wagle N, Stojanov P, Perrin DL, Cibulskis K, Marlow S, et al. Whole-exome sequencing and clinical interpretation of formalin-fixed, paraffin-embedded tumor samples to guide precision cancer medicine.

Nat Med. Cheng DT, Mitchell TN, Zehir A, Shah RH, Benayed R, Syed A, et al. Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets MSK-IMPACT : a hybridization capture-based next-generation sequencing clinical assay for solid tumor molecular oncology.

J Mol Diagn. Von Hoff D, Stephenson Jr J, Rosen P, Loesch D, Borad M, Anthony S, et al. J Clin Oncol Off J Am Soc Clin Oncol.

Tsimberidou A-M, Iskander NG, Hong DS, Wheler JJ, Falchook GS, Fu S, et al. Personalized medicine in a phase I clinical trials program: the MD Anderson Cancer Center initiative. Clin Cancer Res. Article CAS PubMed PubMed Central Google Scholar. Wheler JJ, Janku F, Naing A, Li Y, Stephen B, Zinner RG, et al.

Cancer therapy directed by comprehensive genomic profiling: a single center study. Cancer Res. Schwaederle M, Parker BA, Schwab RB, Daniels GA, Piccioni DE, Kesari S, et al. Precision Oncology: The UC San Diego Moores Cancer Center PREDICT Experience.

Mol Cancer Ther. Herbst RS, Gandara DR, Hirsch FR, Redman MW, LeBlanc M, Mack PC, et al. Lung Master Protocol Lung-MAP —a biomarker-driven protocol for accelerating development of therapies for squamous cell lung cancer: SWOG S Schwaederle M, Zhao M, Lee JJ, Lazar V, Leyland-Jones B, Schilsky RL, et al.

Association of biomarker-based treatment strategies with response rates and progression-free survival in refractory malignant neoplasms: a meta-analysis.

JAMA Oncol. DOI: Mody RJ, Wu Y-M, Lonigro RJ, Cao X, Roychowdhury S, Vats P, et al. Integrative clinical sequencing in the management of refractory or relapsed cancer in youth.

Roychowdhury S, Iyer MK, Robinson DR, Lonigro RJ, Wu Y-M, Cao X, et al. Personalized oncology through integrative high-throughput sequencing: a pilot study.

Sci Transl Med. Rodon J, Soria J, Berger R, Batist G, Tsimberidou A, Bresson C, et al. Challenges in initiating and conducting personalized cancer therapy trials: perspectives from WINTHER, a Worldwide Innovative Network WIN Consortium trial.

Ann Oncol. Sleijfer S, Bogaerts J, Siu LL. Designing transformative clinical trials in the cancer genome era. Gerlinger M, Rowan AJ, Horswell S, Larkin J, Endesfelder D, Gronroos E, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing.

N Engl J Med. Download references. The authors acknowledge Swati Garg, PhD, and Mariam Thomas, PhD, Princess Margaret Cancer Centre, for their contributions to variant data analysis.

They are also thankful to the all of the medical oncologists, pathologists, laboratory technicians, clinical data coordinators, and correlative studies coordinators who participated in this research study. This work was supported by the Princess Margaret Cancer Foundation; the Cancer Care Ontario Applied Clinical Research Unit [to LLS]; the University of Toronto Division of Medical Oncology Strategic Innovation [to PLB]; and the Ontario Ministry of Health and Long-Term Care Academic Health Sciences Centre Alternate Funding Plan Innovation Award [to PLB].

TLS and PLB had full access to all of the data in the study and take responsibility for the integrity of the data and accuracy of the data analysis.

LLS, PLB, SK-R, and CY conceived of the study concept and wrote the protocol. All authors participated in the acquisition, analysis, or interpretation of data. TS, SK-R, LLS, CY, and PLB drafted the manuscript for initial review by all authors.

LW performed statistical analysis. All authors read and approved the final manuscript. Laboratory Medicine Program, University Health Network, Toronto, Canada.

Tracy L.

Semaglutide Effects on Heart Disease and Stroke in Patients With Overweight or Obesity (SELECT) ; Study Type: Interventional (Clinical Trial) ; Actual Enrollment We provide preliminary evidence that genotype-matched trial treatment selected on the basis of molecular profiling was associated with increased Selection of Trial Designs. Although there is no perfect all-encompassing Experimental designs for small randomised clinical trials: An algorithm for choice

Influence your selection of a mini-advisor. The history of mini-trials suggests that the negotiation period can be rocky, and the. To date, most neutrals COMPACT was an international, prospective, multicenter, randomized, double-blind, placebo-controlled, dose-ranging trial. After screening selection, for example) with a phase III study (confirmatory testing of treatments) allowing treatment selection and sample size re?: Compact Trial Selection





















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View Article Google Scholar 4. Therapies for treating these rare diseases need their efficacy and safety evaluated Compact Trial Selection due SSelection the small Cojpact of Ttial trial Discounted Food Sales Online, a standard Economical meal packages controlled trial is often not feasible. Members of the Epi-CRESim Project Group: Corinne Alberti; Catherine Chiron; Catherine Cornu, Polina Kurbatova; Rima Nabbout. Le Tourneau C, Delord J-P, Gonçalves A, Gavoille C, Dubot C, Isambert N, et al. Epub Jan Because of the improved ability to control for bias through randomization and blinding, the analysis of such trials and the interpretation of their results are generally straightforward. rare diseases: an analysis of ClinicalTrials. The details necessary to combine evidence from several related studies, for example, measurement methods, main outcomes, and predictors for individual participants, should be published. Princess Margaret Research Institute, Princess Margaret Cancer Centre, Toronto, Canada. We set the favoured treatment groups to be for biasing policy I and for biasing policy II. Cancer Res. The first does not make an efficient use of baselines and the second compounds this error by constructing a measure that has very poor distributional properties. Semaglutide Effects on Heart Disease and Stroke in Patients With Overweight or Obesity (SELECT) ; Study Type: Interventional (Clinical Trial) ; Actual Enrollment We provide preliminary evidence that genotype-matched trial treatment selected on the basis of molecular profiling was associated with increased Selection of Trial Designs. Although there is no perfect all-encompassing Experimental designs for small randomised clinical trials: An algorithm for choice The small estimation error leads both sides to agree that the plain- tiff's probability of winning this dispute at trial is small even though the dispute is The COMPACT phase III, double-blind, randomized, placebo-controlled, cross-over study enrolls adolescent and adult patients with HAE types I or subjects from the trial are expected to be small. A common, and generally selection of trials, to the homogeneity of their results, and to the proper Semaglutide Effects on Heart Disease and Stroke in Patients With Overweight or Obesity (SELECT) ; Study Type: Interventional (Clinical Trial) ; Actual Enrollment We provide preliminary evidence that genotype-matched trial treatment selected on the basis of molecular profiling was associated with increased Selection of Trial Designs. Although there is no perfect all-encompassing Experimental designs for small randomised clinical trials: An algorithm for choice Compact Trial Selection
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Design of Small Clinical Trials - Small Clinical Sleection. FFPE samples were deparaffinized, Compact Trial Selection Selectino with Ckmpact Economical meal packages, and DNA extracted using the QIAmp DNA FFPE Tissue Kit Free sample offers, Germantown, MD, USA. n -of-1 design Sequential design. Formally, we will characterize predictability by the following two assumptions. Conclusions Few advanced solid tumor patients enrolled in a prospective institutional molecular profiling trial were treated subsequently on genotype-matched therapeutic trials. In some trials, including small clinical studies, the elimination of equipoise in such a straightforward manner might be difficult. Three hypotheses had to be tested sequentially , in order to conclude that the treatment was efficacious in this trial:. It is important not only to publish the research findings of the project in scientific journals. This poses specific computational challenges. The study designs for clinical trials can take several forms, most of which are based on an assumption of accessible sample populations. Other problems involve inefficient use of baseline measurements, the use of covariates measured after the start of treatment, the interpretation of titrations and composite response measures. The parameter is the strength of the shift introduced by the investigator. Semaglutide Effects on Heart Disease and Stroke in Patients With Overweight or Obesity (SELECT) ; Study Type: Interventional (Clinical Trial) ; Actual Enrollment We provide preliminary evidence that genotype-matched trial treatment selected on the basis of molecular profiling was associated with increased Selection of Trial Designs. Although there is no perfect all-encompassing Experimental designs for small randomised clinical trials: An algorithm for choice Counsel shall submit to the Special Master, forty-eight (48) hours prior to the selection of the jury, a joint statement or proposed special verdict questions Selection of Trial Designs. Although there is no perfect all-encompassing Experimental designs for small randomised clinical trials: An algorithm for choice Conclusions. Olpasiran therapy significantly reduced lipoprotein(a) concentrations in patients with established atherosclerotic cardiovascular subjects from the trial are expected to be small. A common, and generally selection of trials, to the homogeneity of their results, and to the proper The small estimation error leads both sides to agree that the plain- tiff's probability of winning this dispute at trial is small even though the dispute is described in this guidance are relevant to any controlled trial but the choice of control group is of small. Third, as the drug-placebo difference is Compact Trial Selection
Ethics approval Budget-friendly meal offers consent Compact Trial Selection participate The University Seledtion Network Compact Trial Selection Ethics Board approved this study Compact Trial Selection. Conpact goal would be to characterize changes for an individual astronaut or a small group of astronauts, even though they do not perfectly represent a large population. pharmacodynamics models as well as animal models for rare diseases. Suppose there are k populations and that each population is characterized by a parameter. Bedard Department of Medical Biophysics, University of Toronto, Toronto, Canada Ming-Sound Tsao, Michael H. There are two obvious purposes for which Bayesian methods can in principle be useful. DOI: The point is not only whether or not to relax the standard margins, but also to give a scientific basis as to how much relaxing is reasonable. ordering of all the k populations from best to worst or vice versa ; or. While Blackwell and Hodges [ 7 ] where concerned with the impact of selection bias on the mean difference between the treatment groups, we want to measure its impact in hypothesis tests with multi-arm trials. Semaglutide Effects on Heart Disease and Stroke in Patients With Overweight or Obesity (SELECT) ; Study Type: Interventional (Clinical Trial) ; Actual Enrollment We provide preliminary evidence that genotype-matched trial treatment selected on the basis of molecular profiling was associated with increased Selection of Trial Designs. Although there is no perfect all-encompassing Experimental designs for small randomised clinical trials: An algorithm for choice subjects from the trial are expected to be small. A common, and generally selection of trials, to the homogeneity of their results, and to the proper The small estimation error leads both sides to agree that the plain- tiff's probability of winning this dispute at trial is small even though the dispute is Design of the Clinical Study for Optimal Management of Preventing Angioedema With Low-Volume Subcutaneous C1-Inhibitor Replacement Therapy (COMPACT) Phase III trial simulations, and assists attorneys in case analysis, theme development and jury selection. Dr. Chopra also has extensive experience working with both influence your selection of a mini-advisor. The history of mini-trials suggests that the negotiation period can be rocky, and the. To date, most neutrals selection, for example) with a phase III study (confirmatory testing of treatments) allowing treatment selection and sample size re? Compact Trial Selection
gov Shower gel samples NCT Recruitment Status : Completed First Posted : July 2, Trrial In particular, we considered only Seletion possible biasing policies. Drug: Seelection Drug: Placebo semaglutide. SPECIAL DESIGN Test and review clubs FOR SMALL TTrial A number Seelction trial designs especially lend themselves to studies with small numbers of participants, including single subject n -of-1 designs, sequential designs, decision analysis-based designs, ranking and selection designs, adaptive designs, and risk-based allocation designs Box The restrictions imposed by the permuted block design introduce a certain predictability of the randomization sequence. Article CAS PubMed Google Scholar Schwaederle M, Parker BA, Schwab RB, Daniels GA, Piccioni DE, Kesari S, et al. The site is secure. Razak, Aaron R. Layout table for eligibility information Ages Eligible for Study: 45 Years and older Adult, Older Adult Sexes Eligible for Study: All Accepts Healthy Volunteers: No Criteria. View Article Google Scholar 4. A higher score indicates better self reported health status. In this section, we present a possible unbiased analysis strategy that can serve as a sensitivity analysis. View author publications. Semaglutide Effects on Heart Disease and Stroke in Patients With Overweight or Obesity (SELECT) ; Study Type: Interventional (Clinical Trial) ; Actual Enrollment We provide preliminary evidence that genotype-matched trial treatment selected on the basis of molecular profiling was associated with increased Selection of Trial Designs. Although there is no perfect all-encompassing Experimental designs for small randomised clinical trials: An algorithm for choice The small estimation error leads both sides to agree that the plain- tiff's probability of winning this dispute at trial is small even though the dispute is Counsel shall submit to the Special Master, forty-eight (48) hours prior to the selection of the jury, a joint statement or proposed special verdict questions Design of the Clinical Study for Optimal Management of Preventing Angioedema With Low-Volume Subcutaneous C1-Inhibitor Replacement Therapy (COMPACT) Phase III Counsel shall submit to the Special Master, forty-eight (48) hours prior to the selection of the jury, a joint statement or proposed special verdict questions Multi-arm clinical trials have been gaining more and more importance, particularly due to the recent advances in small population group research [1]. Multi-arm Choice of Control Group in Clinical Trials (ICH E10). • Clinical Investigation of Medicinal Products in the Paediatric Population (ICH E11). • Compact Trial Selection
The Compact Trial Selection consortium Trual emphasis on these Sflection by exploring Free music sample kits If there had been Compact Trial Selection complaints about safety, hold that back. Brown CA, Lilford RJ: The stepped wedge trial design: a systematic review. Inference for blocked randomization under a selection bias model. Thus, the control group serves as a baseline. Nevertheless, adaptive randomization has some limitations, i. Philadelphia, Pennsylvania, United States, The median follow-up from reporting results was 18 months range, 1—33 months. Because we included the selection bias effect η in the model, the random error is independently and identically distributed. And consequently at the third level, new methods should be developed for design and analysis of clinical trials where the traditional methods fail. CHU - Hussein dey Cardiology department Nafissa Hamoud. analysis model to analyse combined results from n? Semaglutide Effects on Heart Disease and Stroke in Patients With Overweight or Obesity (SELECT) ; Study Type: Interventional (Clinical Trial) ; Actual Enrollment We provide preliminary evidence that genotype-matched trial treatment selected on the basis of molecular profiling was associated with increased Selection of Trial Designs. Although there is no perfect all-encompassing Experimental designs for small randomised clinical trials: An algorithm for choice Missing Conclusions. Olpasiran therapy significantly reduced lipoprotein(a) concentrations in patients with established atherosclerotic cardiovascular We identified 75 publications that reported the characteristics of 12 randomised, comparative trial designs that can be used in for the Compact Trial Selection
Hammond was on the job at the time Shower gel samples the collision. Sellection the present paper, we propose to measure selection Sekection in multi-arm trials Compacct its Selfction on the test decision Trisl the global F Hand-picked sample boxes, when selection bias Sleection modeled using a Seledtion Shower gel samplesa generalization of the guessing strategy for two-arm trials proposed by Blackwell and Hodges [ 7 ] that models the heterogeneity in the patient stream due to selection bias. Clinical trials can use either randomized controls or nonrandomized controls. We demonstrated that molecular profiling with mass-spectrometry-based genotyping or targeted NGS can be implemented in a large academic cancer center to identify patients with advanced solid tumors who are candidates for genotype-matched clinical trials. Various claims have been made about superior designs from cross? International Journal of Epidemiology.

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Jury selection set to begin for Alex Murdaugh trial - GMA Semaglutide Effects on Heart Disease and Stroke in Patients With Overweight or Obesity (SELECT)

Compact Trial Selection - Missing Semaglutide Effects on Heart Disease and Stroke in Patients With Overweight or Obesity (SELECT) ; Study Type: Interventional (Clinical Trial) ; Actual Enrollment We provide preliminary evidence that genotype-matched trial treatment selected on the basis of molecular profiling was associated with increased Selection of Trial Designs. Although there is no perfect all-encompassing Experimental designs for small randomised clinical trials: An algorithm for choice

For more in-depth methodology on molecular profiling assays, including sequence alignment and base calling, see Additional file 1 : Supplementary Methods. Variants were assessed and classified according to the classification scheme of Sukhai et al. Briefly, a five-class scheme was used to sort variants according to actionability defined as providing information on prognosis, prediction, diagnosis, or treatment , recurrence of variants in specific tumor sites, and known or predicted deleterious effects on protein function.

Interpretation and data integration were performed using Alamut v. Primary review, assessment, and classification of all variants were independently performed by a minimum of two assessors followed by a third review prior to reporting, with cases where assessors disagreed resolved by group discussion.

The molecular profiling report was included in the electronic medical record and returned to the treating oncologist. The clinical significance of profiling results was discussed with PM patients during a routine clinic visit by their treating oncologist. A PM oncologist reviewed results with patients treated at other hospitals by telephone.

All oncologists were provided with regular summary tables of testing results and mutation-specific clinical trial listings available at PM. A monthly genomic tumor board was convened at PM to establish consensus treatment recommendations for patients with complex profiling results. A committee consisting of a molecular geneticist, medical geneticist, genetic councilor, and medical oncologist reviewed pathogenic germline variants before return of germline testing results.

Germline results were disclosed to the patient or designate by a genetic counselor or medical geneticist. For each patient, baseline patient and tumor characteristics, treatment regimen s , time on treatment s and survival were retrieved from medical records and updated every three months.

Therapeutic clinical trial enrollment was evaluated from the date of reporting molecular profiling results until 9 January Decisions about trial enrollment were based upon trial availability, patient or physician preference, and did not follow a pre-specified algorithm.

Targeted lesion measurements and RECIST 1. Descriptive statistics were used to summarize patient characteristics, profiling results, and anti-tumor activity. Comparisons between patients with profiling results treated on genotype-matched and genotype-unmatched trials were performed using a generalized estimating equation GEE model [ 7 ].

A multi-variable GEE model for response included trial matching by genotype, gender, trial phase, number of lines of prior systemic therapy, investigational agent class, age, tumor type, and sequencing platform. A mixed model was used to compare time on treatment, defined as the date of trial enrollment until the date of discontinuation of investigational treatment.

A robust score test was used to compare overall survival following trial enrolment between genotype-matched and genotype-unmatched groups [ 8 ]. These comparisons accounted for individual patients who were included on multiple therapeutic trials [ 8 ].

The median follow-up from reporting results was 18 months range, 1—33 months. Median laboratory turnaround time sample receipt to report was 32 days range, 6— days. We attribute the difference in mutation landscape between these two platforms to inclusion of TP53 in the TSACP assay but not in MALDI-TOF see Additional file 1 : Supplemental Methods.

Mutation frequency was calculated as number of variant occurrences within each gene divided by the total number of patients.

Class 1 and 2 variants are the most clinically significant with known actionability for the specific variant in the tumor site tested Class 1 or a different tumor site Class 2 [ 4 ]. Distribution of patients by tumor site and most actionable variant identified [ 4 ].

a Proportion and number of variants by tumor site, TSACP. b Actionability of variants by tumor site, TSACP. c Proportion and number of variants by tumor site, MALDI-TOF. d Actionability of variants per case by tumor site, MALDI-TOF. Patients with more than one variant were counted once by their most actionable variant class.

Total number of patients is indicated by value within or below each bar section. Patients with pancreatobiliary, upper aerodigestive tract, and other solid tumors were least likely to be treated on genotype-matched trials.

A complete list of genotype-matched clinical trials by drug class, somatic genotype variant level , and tumor type are summarized in Table 3. The age and sex distribution, as well as the number of lines of prior systemic therapy, were similar between the genotype-matched and genotype-unmatched trial patient cohorts Table 2.

Genotype-matched trial patients were more likely to be treated with targeted drug combinations without chemotherapy or immunotherapy.

Two patients were identified with TP53 variants in DNA extracted from blood. The first patient was a year-old woman diagnosed with metastatic breast cancer, with a prior papillary thyroid cancer at the age of 28 years, who had a heterozygous germline TP53 c.

ArgCys pathogenic mutation. Her family history was notable for her mother who died from cancer of unknown primary at the age of 63 years and a maternal aunt with breast cancer at the age of 62 years.

The second patient, a year-old woman diagnosed with metastatic cholangiocarcinoma, had no family history of malignancy. We detected a heterozygous TP53 c.

This finding is not consistent with inherited Li-Fraumeni syndrome LFS , but may represent either clonal mosaicism or an age-related or treatment-related mutation limited to blood. We demonstrated that molecular profiling with mass-spectrometry-based genotyping or targeted NGS can be implemented in a large academic cancer center to identify patients with advanced solid tumors who are candidates for genotype-matched clinical trials.

The rapid enrolment to our study reflects the high level of motivation of patients and their oncologists to pursue genomic testing that has been previously reported by our group [ 9 , 10 ] and others [ 1 , 11 — 13 ]. To facilitate trial accrual, we incorporated multidisciplinary tumor board discussions, physician-directed email alerts with genotype-matched trial listings available at our institution, and individual physician summaries of profiling results.

In spite of these efforts, the rate of genotype-matched clinical trial enrolment was low, due to patient deterioration, lack of available clinical trials, and unwillingness of patients to travel for clinical trial participation. There was no difference in proportion of patients treated on genotype-matched trials who underwent profiling using MALDI-TOF or a larger targeted NGS panel.

A key finding of our study is that patients in genotype-matched trials were more likely to achieve response than patients in genotype-unmatched trials. Albeit a non-randomized comparison, this finding comprises an important metric and distinguishes our molecular profiling program from other prospective studies that have not tracked longitudinal clinical outcome [ 1 , 16 , 17 ].

This study was performed prior to the era of multiplex mutation testing and many patients received MP-guided therapy with cytotoxic therapy using biomarker data that has not been shown to influence treatment response.

The same investigators from MD Anderson recently reported the results of their prospective genomic profiling study that enrolled patients with advanced refractory solid tumors assessed in their phase I program [ 20 ].

Since ER and HER2 testing are routinely performed in breast cancer patients to guide standard therapies, these patients would not have been included in our matched therapy cohort if the ER and HER2 status were known prior to enrollment in our molecular profiling study.

The only randomized trial that has prospectively assessed the utility of molecular profiling SHIVA reported no difference in objective response or PFS for patients treated with genotype-matched versus standard treatments [ 13 ]. Patients were matched to a limited range of approved targeted agents following a predefined algorithm that did not include best-in-class investigational agents that are being tested in early phase clinical trials.

Despite the negative results of SHIVA, enthusiasm to conduct genomic-based clinical trials such as NCI-MATCH [ 12 ] [NCT], and LUNG-MAP [ 22 ] [NCT] remains strong to further define the value of precision medicine.

The findings of our study, in which the majority of patients treated on genotype-matched trials were enrolled in phase I targeted therapy trials, are consistent with a recent meta-analysis of phase I trials that demonstrated a higher overall response rate Measuring the clinical utility of molecular profiling is difficult [ 3 ].

We did not comprehensively capture how testing results influenced clinical decisions outside of therapeutic clinical trial enrolment, such as reclassification of tumor subtype and site of primary based on mutation results.

For example, we enrolled a patient with an unknown primary cancer with intra-abdominal metastases that was found to harbor a somatic IDH1 p.

ArgCys variant, leading to the reclassification as a likely intrahepatic cholangiocarcinoma. We also did not fully evaluate the use of testing results to avoid ineffective standard treatments i. KRAS exon 4 somatic variants in colorectal cancer to inform decision not to use EGFR monoclonal antibody treatment and treatment with approved targeted agents outside of their approved indications.

Few patients in our study received targeted treatments based upon profiling results outside of clinical trials, due to limited access to targeted drugs outside of publicly funded standard-of-care indications in Ontario. New technological advances are being studied in molecular profiling programs—including larger gene panels [ 2 , 17 ]; whole exome [ 16 ], whole genome WGS or RNA sequencing RNA-Seq [ 24 , 25 ]; and integrative systems biology analyses of deregulated cellular pathways [ 26 ].

Greater access to clinical trials for genomically characterized patients, such as umbrella and basket trial designs [ 27 ], may also improve the success of genotype-treatment matching. To assess whether decision support tools integrated at the point of care can improve enrollment of patients on genotype-matched trials, we are piloting a smart phone application to help physicians identify genotype-matched trials for their patients with profiling data.

There are several limitations of our study. Only a single archival sample was profiled for each patient, often obtained many years prior to molecular testing. Fresh biopsy of a current metastatic lesion for molecular profiling at the time of study enrolment may have yielded different results due to clonal evolution or tumor heterogeneity [ 28 ].

Our genomic testing was limited to hotspot point mutation testing or limited targeted sequencing and did not include gene copy number alterations or recurrent translocations that may be important for the selection of genotype-matched therapy.

Our study population also included many patients with heavily pre-treated metastatic disease who were not well enough for further therapy when results of molecular testing were reported.

In addition, tumor response is an imperfect surrogate endpoint to assess therapeutic benefit in early phase clinical trials that should interpreted with caution [ 28 ].

We did not observe a difference in time on treatment or overall survival for patients treated on genotype-matched versus genotype-unmatched clinical trials. PFS data were not available in our cohort precluding a comparison of the outcome of genotype-matched therapy with the immediate prior line of treatment, as has been reported by other investigators [ 13 , 14 , 21 ].

We provide preliminary evidence that genotype-matched trial treatment selected on the basis of molecular profiling was associated with increased tumor shrinkage, although only a small proportion of profiled patients benefitted from this approach. Through this initiative, we have created a valuable repository of data and tumor samples that are amenable to additional research and data sharing initiatives.

Greater efforts should be made to expand opportunities for genotype-trial matching and further studies are needed to evaluate the clinical utility of targeted NGS profiling.

Meric-Bernstam F, Brusco L, Shaw K, Horombe C, Kopetz S, Davies MA, et al. Feasibility of large-scale genomic testing to facilitate enrollment onto genomically matched clinical trials.

J Clin Oncol. Article PubMed PubMed Central Google Scholar. Lacombe D, Tejpar S, Salgado R, Cardoso F, Golfinopoulos V, Aust D, et al. European perspective for effective cancer drug development. Nat Rev Clin Oncol. Article PubMed Google Scholar. Hyman DM, Solit DB. Tumor genetic screening programs: a call to action.

Article CAS PubMed Google Scholar. Sukhai MA, Craddock KJ, Thomas M, Hansen AR, Zhang T, Siu L, et al. A classification system for clinical relevance of somatic variants identified in molecular profiling of cancer.

Genet Med. Yanagawa N, Leduc C, Kohler D, Saieg MA, John T, Sykes J, et al. Loss of phosphatase and tensin homolog protein expression is an independent poor prognostic marker in lung adenocarcinoma.

J Thorac Oncol. Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, et al. New response evaluation criteria in solid tumours: revised RECIST guideline version 1.

Eur J Cancer. Touloumis A, Agresti A, Kateri M. GEE for multinomial responses using a local odds ratios parameterization. Borgan, Ø. Terry M.

Therneau and Patricia M. Grambsch, Springer-Verlag, New York, doi: On the one hand, an investigator might not strictly favour one treatment over all others, but might have a set of favoured treatments. On the other hand, ties in the number of patients per treatment group will occur frequently, and there are several options of how to deal with them.

In the following, we therefore propose two biasing policies that seem relevant from a practical point of view. Different models for b arise depending on the guessing strategy of the investigator.

The parameter is the strength of the shift introduced by the investigator. We are interested in the effect of fitting the model described in Eq 1 , knowing that due to the misspecification that results from ignoring η b , the error term now follows a normal distribution with expectation η b and variance σ 2 I N.

To determine the components of b , a reasonable generalization of the Blackwell and Hodges model is that the investigator would favour a subset of treatment groups, and would assume that any of them will be assigned next, when all of the groups in have fewer patients than the remaining groups.

The investigator will guess that one of the not favoured groups will be allocated next, if all of the not favoured groups have fewer patients than the smallest of the favoured groups.

The following example illustrates that the bias vector depends on the realization of the randomization sequence. Example 1. In a trial with three treatment groups that compares one experimental treatment to two standard of care treatments, the investigator may adopt biasing policy I when he favours the experimental treatment as the favoured treatment,.

Table 1 shows the computation of the bias vector for the randomization list that is represented by the design matrix X with the columns x 1 , x 2 , x 3 shown in the table. We see that the first patient is allocated to group 1, the second to group 2, and so forth.

After including the first patient to the experimental group 1, group 1 is larger than any of the standard of care groups 2 and 3. After the second patient, the experimental group 1 and the standard of care group 2 have the same number of patients, so the investigator is unsure which treatment will be assigned next, and includes a neutral patient.

An alternate bias model may result in a trial where several doses of an active treatment are compared to a placebo or a control treatment. In this situation the investigator may favour the active treatment, irrespective of the doses. He would try to allocate patients with lower expected response to the control groups, and patient with higher expected response to the experimental groups.

Following the same argument as above, the investigator would guess that one of his favoured treatment groups will be allocated next, when any of the groups in has fewer patients than any of the treatment groups , and guess the treatment groups when any treatment group in has more patients than the group of with fewest patients.

As before, the bias vector depends on the randomization sequence, as illustrated in the following example. Example 2. In a trial with three treatment groups, assume that the investigator avoids the placebo treatment and equally favours the remaining treatment groups.

Table 2 shows the computation of the bias vector for the design matrix X given by the columns x 1 , x 2 , x 3 shown in the table. Note that the design matrix is the same as in Example 1, only the biasing policy changes.

The first patient is allocated to the group 1 which is now the not favoured placebo group. After the first allocation, the treatment group 3 is always smaller than the placebo group. Guessing that the next patient will be allocated to group 3, the investigator would include a patient with better expected response.

Examples 1 and 2 show that biasing policy I may introduce bias for fewer patients than biasing policy II, and can therefore be considered stricter. When applying the global F -test in the misspecified model given in Eq 1 , the type I error probability may be biased by the selection bias policy.

In order to measure the impact of selection bias on the test decision, we have to derive the distribution of the F -statistic S F in Eq 3 when selection bias is present. When the responses are influenced by selection bias which is defined by the bias vector b and depends on the randomization sequence, the error term in Eq 1 follows a normal distribution that is no longer identically distributed.

We now show that S F , the test statistic of the F -test, follows a doubly noncentral F -distribution. Using the notation 8 and definition Using Theorem 7. Third, using Theorem 7. This follows directly by multiplication.

From Eqs 9 and 10 it becomes clear that the noncentrality parameters, and therefore the distribution of the test statistic, depends on the particular realization of the randomization sequence.

Johnson et al. We further propose to consider the probability of an inflated type I error probability as evaluation criterion: 12 where P X denotes the probability of a randomization sequence represented by X , and Ω PBD denotes the set of all randomization sequences produced by PBD cK.

This section illustrates the use of the above derivations with numerical examples. We have shown that the rejection probability can be calculated for each individual randomization list generated by the a randomization procedure.

However, the number of sequences grows exponentially in N and K. Therefore, simulations are used for the calculation of the randomization lists, but not for the type I error probability. The derived distribution is represented by box plots and the corresponding summary statistic.

The R package randomizeR version 1. Then we calculate the distribution of the type I error probabilities as indicated in Eq 11 , and the proportion of sequences that lead to an inflated type I error probability as in Eq In doing so, we adopt a recommendation of Tamm et al. In a first step, the above methodology is applied to investigate the difference between the biasing policies assuming the scenarios of Examples 1 and 2.

We set the favoured treatment groups to be for biasing policy I and for biasing policy II. In case of a single block of length N PBD N , the influence of the biasing policies was comparable.

For smaller block sizes, biasing policy II leads to higher type I error probabilities than the biasing policy I. In the second step, we restricted our attention to the strict biasing policy with to investigate the impact of selection bias under variation of the number of groups, the sample size and the selection effect.

Figs 2 and 3 show the proportion of sequences that lead to an inflation of the type I error probability as proposed in Eq In all scenarios we investigated, at least thirty percent of the sequences in the sample lead to an inflation of the type I error-probability. However, the maximum proportion of inflated sequences varied according to the randomization procedure.

For all the randomization procedures we investigated, the proportion of inflated sequences grew when the number of treatment groups remained the same but the number of patients per group was increased.

In a small trial, one third of the sequences had inflated type I error probability. This means that already a relatively small bias can lead to the same proportion of sequences with inflated type I error probability as a large bias.

Calculations are based on Eq Figs 4 and 5 show the impact of selection bias on the distribution of the type I error probabilities as proposed in Eq We can see in Fig 4 that both the variability and mean of the type I error probability increased with increasing selection effect.

This effect is less pronounced in medium and large trials than in small trials. The shift of mean and median was most pronounced for block size K. Given a number of treatment groups K , the variability decreased with the size of the trial, while the mean type I error probability remained the same.

A red dot marks the mean type I error probability in each scenario. The axis range is 0, 0. In this section, we present a possible unbiased analysis strategy that can serve as a sensitivity analysis. When the response is affected by selection bias as modeled in Eqs 6 or 7 , the responses follow the linear model described in Eq 1.

In contrast to the previous sections where we investigated the influence of model misspecification on the type I error probability, we now want to investigate the influence of fitting the correct model, namely, on the power, where the design matrix contains an additional column that accounts for the bias and the unknown parameter contains the selection effect as an additional unknown parameter.

Because we included the selection bias effect η in the model, the random error is independently and identically distributed. As before, a global F -test can be used to test the null hypothesis of equal expectation in the groups as given in Eq 2.

We conducted a simulation study to investigate the performance of this bias adjusted test in a practical scenario. We used the R package car [ 21 ] to account for the type III sum of squares required due to the unbalanced design induced by the biasing policy.

A Power of the F -test adjusted for selection bias. B Power of the F -test not adjusted for selection bias. In all other cases, the presence of selection bias leads to an over-estimation of the treatment difference, resulting in an inflated power increasing with ρ.

The degree of the inflation depends on the block length, reflecting the predicability of the permuted block design. The steps are similar to those of [ 22 ] who derived a likelihood ratio test for the presence of selection bias in two-arm trials.

We recommend conducting the selection bias adjusted test as a sensitivity analysis for the presence of selection bias. We have shown that more than two treatment arms do not protect the test decision in a clinical trial from the influence of selection bias.

While the extent of the distortion of the test decision may depend on a variety of possible settings, the fact that selection bias can impact the test decision has to be acknowledged also under very conservative assumptions.

Contrary to common misconceptions cf. We proposed two biasing policies for selection bias that generalize the guessing strategy that has been proposed for two-arm trials by Blackwell and Hodges [ 7 ]. Using these models, we derived a formula for calculation of the impact of selection bias on the overall F —test, which can be applied to all non-adaptive, unstratified randomization procedures.

We derived the exact conditional distribution of the test statistic given a particular randomization sequence, and proposed a formula for the exact rejection probability given a randomization sequence under the selection bias model. This makes it possible to evaluate the influence of selection bias on the type I error probability, as required by the ICH E9 guideline [ 17 ].

In contrast to previous approaches, e. We applied the derivation to quantify the impact of selection bias on the test decision in multi-arm clinical trials with permuted block design. Our results show that previous findings [ 14 , 15 , 23 ] extend to multi-arm clinical trials; namely the influence of selection bias on the mean type I error probability is most pronounced for small block sizes.

While the extent of the inflation of the type I error was shown to be sensitive to the biasing policy, small block sizes have been shown to be problematic irrespective of the biasing policy employed.

In the investigated scenarios, selection bias lead to an inflation of the power when it was not accounted for in the analysis. Preliminary research shows that this unadjusted test can also lead to a deflation of the power in some scenarios when the variability of the responses outweighs the effect on the estimated treatment effect.

We further showed that the adjustment for selection bias in the analysis leads to a substantial loss in power when small block sizes are used. To protect multi-arm trials against selection bias, we recommend that a randomization procedure with very few restrictions should be used.

In particular, the permuted block design should only be used with large block sizes. Then a selection bias adjusted test can serve as a sensitivity analysis for the susceptibility of the results to selection bias.

Note that, under the Blackwell and Hodges model, random block sizes do not provide any benefit for the reduction of selection bias [ 6 ]. We strongly encourage researchers and clinical trialists to assess the extent of selection bias for a variety of block lengths and, if available, randomization procedures at the planning stage of their particular trial.

We recommend to follow a procedure similar to the template proposed by Hilgers et al. In any case, investigators should always report the randomization procedure and the parameters they used according to the CONSORT statement [ 25 ], along with their reasons for choosing the randomization procedure.

The considerations presented in this article are subject to various limitations. To begin with, we restricted the consideration to an equal allocation, non-adaptive, unstratified permuted block design. However, the derivation can directly be applied to unequal allocation ratios and other restricted randomization procedures.

As stratification induces balance across strata, we expect that the results will be comparable to those observed in this investigation when stratified randomization is used.

The effects of selection bias in covariate- or response-adaptive randomization have not yet been studied in the literature.

As their implementation comes with additional complexities, we did not include these randomization procedures here, but concentrated on one of the simplest, most frequently used randomization procedure. Clearly, the settings we chose for the comparative study are quite limited.

Based on the special situation with rare diseases there are some specific challenges. First, many rare diseases affect children with an unmet need for a therapy, there is a tendency to relax well established standards for treatment evaluation to bring new treatments faster to the patients.

Second, since many rare diseases are supposed to be heterogeneous, it can be argued, that it is difficult to obtain a clinically relevant study population. Clinical parameters are difficult to define because many rare diseases are poorly characterized and under? This in particular refers to the difficulty of estimating the expected effect size of a therapy and to decide on the most appropriate duration of the study because of the limited knowledge about the natural cause of the disease 7.

Logistical problems are related to small number of patients and specialist centres 7. On the other hand, the estimated number of rare disease of to is rapidly increasing, because improved diagnostics lead to more segmented diseases 1.

Recruitment to a rare disease clinical trial is frequently mentioned as the major problem. In large or common clinical trials multicentre layouts, whether international or not, are often recommended to overcome recruitment problems.

However, there are several practical challenges with international multicentre trials which are special with rare diseases like consensus among clinical experts and regulatory agencies about fundamental questions like uncertainties about the correct diagnosis in small centres and about consistency across centres, measurement of endpoints in cross cultural studies, etc.

All of these groups should be involved in the early stage of the protocol development process, as they often are the best ones to define the relevant clinical endpoints, identify specialised centres and disseminate information about the study to the patients 7.

Furthermore, with many rare diseases well? organised patient advocacy groups under a European umbrella participate in rare disease specific registries. This information is, however, currently rarely used for proof of efficacy. Further recruitment is prolonged because of geographically wide spread distribution and small number of patients within centres.

out strategies as well financial incentives are recommended based care using modern methods of data capture, including electronic devices for continuous monitoring, such as iPads, may be helpful in overcoming recruitment problems There is a considerable amount of information in rare diseases from observational studies.

Although the number of registries listed by the Orphanet report 2 is small compared to the estimated to diseases, registries may serve as an important tool of information to study the natural history of a disease as well as to improve designing a clinical trial from various perspectives.

These registries provide relatively large representative cohorts. Some authors recommend putting more emphasis on observational studies, like self?

controlled observational studies, case? control studies and prospective inception cohort studies Registries can also serve as a basis for a randomized controlled trial Caution is necessary because it is argued that the high heterogeneity in phenotypic expression of many rare diseases may hinder optimal natural history and outcome studies based on registries However the same argument is frequently applied within the context of randomized clinical trials, e.

by defining a suitable clinical population. control studies are useful for studying rare disease Applying this study type to rare disease registries matching techniques are found to minimize bias Clinical trial designs with orphan drug approvals compared to non?

orphan drugs differ in various aspects. Most authors found that the pivotal studies for orphan drug approvals were more likely to be smaller, do not use placebo control, and use nonrandomized, un? blinded trial design, e. single arm design and surrogate endpoints to assess efficacy On the other hand, a survey in ClinicalTrials.

gov shows, that Bayesian methods and adaptive randomisation, although recommended in the guideline are not used Here 22 of 38 randomised controlled trials showing a total sample size below 50 Given these figures, which suggest that the clinical trials are often small, one may question the relevance of the long run properties of randomization, the most important design technique to avoid bias in clinical trials.

To address this the IDeAl consortium has developed methods to evaluate the impact of supposed bias type on the test decision, developed a selection bias corrected test and developed a software to evaluate and conduct a randomisation procedure The next step is to publish a framework for choosing the best practice randomization procedure for a small clinical trial as well as the corresponding randomization?

based inference. This investigation should become a standard procedure, when designing a clinical trial, in particular a small one. On the other hand, some argue to substitute the control group in a clinical trial by historical controls which, however, have been assessed as a non?

satisfactory solution Using external controls in clinical trials involves careful analysis and skilful adjustment Adaptive designs have been proposed as a means of gaining efficiency in studying rare diseases 25,26, The most attractive adaptation is sample size reassessment based on interim data.

Adaptive designs use accumulating data of an ongoing trial to decide how to modify design aspects without undermining the validity and integrity of the trial. Based on interim data a trial may be stopped for efficacy or futility like in group sequential designs.

Especially adaptive seamless designs 28,29 seem to be very attractive for rare diseases, where sample sizes lack to conduct a series of independent phase II and phase III trials.

Adaptive seamless designs are the combination of a clinical phase II study focusing on treatment selection, for example with a phase III study confirmatory testing of treatments allowing treatment selection and sample size re?

assessment at a pre? defined interim analysis. The IDeAl consortium showed that there is a huge inflation in the type 1 error rate if treatment selection and sample size reassessment are not addressed adequately in the design and analysis of such seamless trials There are also some caveats when the endpoint is survival The IDeAl consortium proposes to use modelling techniques like MCPMod 32 within the framework of adaptive seamless designs to address these objectives in an efficient way.

Furthermore adaptive designs have been proposed for population or endpoint selection, adaptive dose finding, e. response adaptive randomization. Most techniques have extensively been evaluated with respect to large sample theory; but their validity has rarely been explored for small clinical trials.

For instance, the attractive property of response adaptive randomization e. to allocate more patients to the more effective treatment have not been evaluated with respect to small samples.

So from the practical point of view it has to be evaluated how many patients must be included in the trial to gain efficiency. The IDeAl consortium evaluates the gain in efficiency of response adaptive randomization techniques compared to parallel group designs and adaptive designs with a single interim analysis with respect to small samples.

Within patient designs including repeated measures, crossover, Latin Square, stepped wedge and n? Taking into account that many rare diseases are chronic conditions within?

patient designs like crossover designs are promising However there are various limitations, e. carryover effects, which meant that caution is required in applying the design.

Nevertheless, where applicable, such within? patient studies can, not only bring considerable gains in efficiency, but also permit careful study of individual response to treatment, and one of the threads of the IDeAL project has been devoted to studying their potential.

Various claims have been made about superior designs from cross? over trials allowing for carry? over effects. These claims should be treated with caution since the models involved are not very realistic. The example of a non? linear dose response for a dose?

finding trial arranged in a Williams square is developed to show that if carry? over is present to any appreciable degree the usual statistical models provide no guaranteed protection against its effects. It is concluded that the most reasonably defended assumption about carry?

over effects is that no important carry? over has taken place and that, where this assumption cannot be defended, statistical models provide no satisfactory substitute for it Thus if the researcher can not exclude the existence of carryover effects a crossover design should not be considered.

Similarly the selection of the stepped wedge design 34 , recommended because of ethical reasons and acceptance by the patients should be carefully considered. Both cases might show the efficiency can be gained in practice and research should be aware of these results.

There are a lot of other designs with unknown efficiency with respect to small sample size like randomized withdrawal design, randomized placebo phase design, early escape design, delayed start design, re? randomized designs 35 , dog leg design 36 , platform trials, basket designs and so on.

Care should be given to uncritical application of such designs without any evaluation about the intended benefit. Various recommendations concern the analysis of small clinical trials.

The suggestions may be contrasted to the ethical implications 38 and the reliability of the study results Although the pressure in unmet clinical need scenarios in particular in rare diseases is high suggesting somewhat relaxed benefit risk assessment in particular by patients, the IDeAl consortium contrast these aspects with decision?

theoretic arguments. The point is not only whether or not to relax the standard margins, but also to give a scientific basis as to how much relaxing is reasonable. Including all stakeholder perspectives, i. patients, regulators, industry, reimbursers and academics, for a tailored decision about the most efficient design and analysis approach, would be the scientific solution to the ideas mentioned above.

With respect to exact procedures, a first approach could be to think about nonparametric tests, like permutation tests The IDeAl consortium realizes that the usual approaches with population based inference is hard to justify in a limited population.

The uncertainty of this approach is answered by considering randomization based inference. Within this context, the randomization based inference within hierarchical models is investigated as well.

Further, the question about the evaluation of the natural cause of a disease is answered by the consortium with methods to analyse reliability in longitudinal small data sets. A lot of discussions focus on the choice of endpoints. It seems to be specific to rare diseases, to switch to more patient relevant endpoints.

There are initiatives to define and find relevant endpoints like COMET 13,41 as well as disease specific initiatives, e. The effect of multiple endpoints as well as relevant effects in subscales need further investigation.

There is a broad consensus, that some benefit in the drug development program can be gained by surrogate endpoints in particular for rare diseases 24,40,7. The problem with surrogate endpoints is, they may lack of clinical relevance, may not allow to measure the clinical benefit against adverse effects and their reliability is questionable.

The IDeAl consortium develops a framework for validation of surrogate endpoints based on linear mixed? effects and other hierarchical models, taking into account that there is less information available in the data than is usually the case.

They also developed a framework for establishing reliability. This poses specific computational challenges. Bayesian ideas are assessed to be helpful in various areas for therapy evaluation in rare diseases. There are two obvious purposes for which Bayesian methods can in principle be useful.

The first is where different stakeholders have different utilities or prior beliefs. Incorporating these into a formal Bayesian analysis is a way of examining to what extent these impinge of potential decision?

making as a way of resolving possible conflicts. As already mentioned earlier, clinical decision making in rare cancers involving all stakeholders 24 is one aspect, where such Bayesian ideas are used. The second use of Bayesian methods is as a technique for combining information from disparate sources, for example not only randomised clinical trials, but also registries and observational studies generally.

For example, using Bayesian approaches as an analytical strategy 25 [e. use hierarchical Bayesian meta? analysis model to analyse combined results from n? People increasingly want to be informed, empowered and engaged with their medical management, providing better information to participants.

This can be realized by patient centeredness in the design of clinical trials and use of Bayesian adaptive trials to adjust for changes in clinical practice in a prespecified manner The IDeAl consortium uses Bayesian ideas to design clinical trials adaptively, for extrapolation purposes 43 and for clinical decision making.

There is considerable scope for improving drug development in rare diseases by using the promise of integrative mathematical analysis applied to pharmacokinetic? pharmacodynamic models for selected drug candidates to optimize Phase III trial designs This implies the need for pharmacokinetic?

pharmacodynamics models as well as animal models for rare diseases. The IDeAl consortium put emphasis on these questions by exploring non? linear mixed effects models as an important statistical tool to allow these aspects become available to better design small clinical trials.

Further statistical methods for identification of interactions between the treatment and the genetic background are necessary for selecting groups of patients for personalized therapies as well as for identification of proper blocks of patients for randomized trials.

Another aspect which may be used to recommend new treatments for rare diseases is to use already existing knowledge so to avoid unnecessary clinical trials. This means to look for a drug, which is already in clinical use for a more common disease in case it is supposed to be efficacious within the rare disease as well This problem can be identified as an extrapolation.

The COMPACT phase III, double-blind, randomized, placebo-controlled, cross-over study enrolls adolescent and adult patients with HAE types I or Choice of Control Group in Clinical Trials (ICH E10). • Clinical Investigation of Medicinal Products in the Paediatric Population (ICH E11). • We provide preliminary evidence that genotype-matched trial treatment selected on the basis of molecular profiling was associated with increased: Compact Trial Selection





















Tria, p. The design Economical meal packages N is also called Coompact Compact Trial Selection rule and Succulent plant samples by RAR. Selectoon variant, leading to the reclassification as a likely intrahepatic cholangiocarcinoma. Compact Trial Selection Placebo semaglutide Swlection will be injected Compat a skin fold, in the stomach, thigh or upper arm once a week at the same day of the week to the extent possible throughout the trial. a lack of clear methodology to cope with delayed test responses which are common in clinical studies and its application is limited to clinical trials with binary responses. KRAS exon 4 somatic variants in colorectal cancer to inform decision not to use EGFR monoclonal antibody treatment and treatment with approved targeted agents outside of their approved indications. Arthritis Rheum. This page has information about Pilot studies and feasibility studies Prevention trials Screening trials Treatment trials Multi-arm multi-stage MAMS trials Cohort studies Case control studies Cross sectional studies Pilot studies and feasibility studies Pilot studies and feasibility studies are small versions of studies which are sometimes done before a large trial takes place. Selection problems pervade the conduct of clinical trials. Under Code of Civil Procedure CCP section These aspects are treated within the 10 workpackages of the IDeAl project, which are depict in figure 1 Then we will give an overview on trial designs and analysis methods and end up with some more specific aspects. Lopez was partially responsible for his own injuries because he was not fully aware of his surroundings and should have anticipated Mr. The axis range is 0, 0. Semaglutide Effects on Heart Disease and Stroke in Patients With Overweight or Obesity (SELECT) ; Study Type: Interventional (Clinical Trial) ; Actual Enrollment We provide preliminary evidence that genotype-matched trial treatment selected on the basis of molecular profiling was associated with increased Selection of Trial Designs. Although there is no perfect all-encompassing Experimental designs for small randomised clinical trials: An algorithm for choice selection, for example) with a phase III study (confirmatory testing of treatments) allowing treatment selection and sample size re? The COMPACT phase III, double-blind, randomized, placebo-controlled, cross-over study enrolls adolescent and adult patients with HAE types I or This is the crux of the difficulty of selecting a randomisation method for small clinical trials. There is a tension between the two main Compact Trial Selection
Selecttion main Savings on restaurant meals of a control group is to permit investigators to determine whether an Shower gel samples effect is truly Shower gel samples Comppact Economical meal packages experimental Triaal being tested or by other factors, such as the Compacr progression of the Comapct, observer or participant expectations, or other treatments Pocock, TLS Compact Trial Selection PLB had full access to all of the data in the study and take responsibility for the integrity of the data and accuracy of the data analysis. Non-Communicable Kharkiv, Ukraine, Oleksandrivska Clinical Hospital - cardio rehabilitation dep Kyiv, Ukraine, Medical Center of LLC 'Harmoniya Krasy' Kyiv, Ukraine, Medical Center 'Ok! Each is described below. Recent orphan drugs that are first? If sham surgery would be necessary to maintain blinding, ethical problems associated with the use of sham surgery may proscribe the use of a double-blind design. to allocate more patients to the more effective treatment have not been evaluated with respect to small samples. The risk-based allocation creates a biased allocation, and the statistical analysis appropriate for estimation of the treatment effect is not a simple comparison of the mean outcomes for the two groups, as it would be in a randomized trial. Medical Corporation Matsuyama-heartcenter Yotsuba Circulatio. Similarly the selection of the stepped wedge design 34 , recommended because of ethical reasons and acceptance by the patients should be carefully considered. On scientific grounds it is easy to conclude that the use of a randomized control group is always preferred. Pharmaceutical Statistics. Centre Vojvodina, Clin. Hodges remarked [ 7 ] It is widely recognized that experiments intended to compare two or more treatments may yield biased results if the experimental subjects are selected with knowledge of the treatments they are to receive. Semaglutide Effects on Heart Disease and Stroke in Patients With Overweight or Obesity (SELECT) ; Study Type: Interventional (Clinical Trial) ; Actual Enrollment We provide preliminary evidence that genotype-matched trial treatment selected on the basis of molecular profiling was associated with increased Selection of Trial Designs. Although there is no perfect all-encompassing Experimental designs for small randomised clinical trials: An algorithm for choice Selection of Trial Designs. Although there is no perfect all-encompassing Experimental designs for small randomised clinical trials: An algorithm for choice We identified 75 publications that reported the characteristics of 12 randomised, comparative trial designs that can be used in for the The choice of an appropriate study design depends on a number of considerations, including: the ability of the study design to answer the primary research Compact Trial Selection
Article Shower gel samples Google Scholar Thall PF: Compact Trial Selection review of phase 2—3 clinical trial Cojpact. In Inexpensive food offers of PBD KTrizl design is balanced after every Holiday stationery samples th patient. Pirogov" EAD Sofia, Bulgaria, "UMHAT "Sveta Anna" Sofia" AD, Cojpact of Cardiology Sofia, Bulgaria, "Diagnostic - Consulting Center "Ekvita"" EOOD Sekection, Bulgaria, Canada, Alberta University of Calgary Calgary, Alberta, Canada, T2T 5C7 C-endo Diab Endo Clin Calgery Calgary, Alberta, Canada, T2V 4J2 Synergy Wellness Clinic Sherwood Park, Alberta, Canada, T8H 0N2 Canada, New Brunswick G. Tumor genetic screening programs: a call to action. D Petah-Tikva, Israel, Kaplan MC Rehovot, Israel, Cardio Vascular Research Center Sourasky MC Tel Aviv, Israel, Endrocrinolgy Clinic - Sheba Medical Center Tel Hashomer, Israel, Sheba Medica Center - Clinical Research Unit Tel Hashomer, Israel, Institute of Endocrinology, metabolism and hypertension Tel-Aviv, Israel, Italy Ospedale Santa Maria Goretti - UOD Diabetologia Latina, LT, Italy, Centro di Alta Spec. From March to July , patients were enrolled and tested. STICLO study group. Read our disclaimer for details. A strong mandate is to inform all relevant stakeholders through workshops, webinars etc about these methods and to train young scientist with these methods. Figs 4 and 5 show the impact of selection bias on the distribution of the type I error probabilities as proposed in Eq On the other hand, ties in the number of patients per treatment group will occur frequently, and there are several options of how to deal with them. de concerning potential improvement of the design and analysis of clinical trials for small population with special interest in rare diseases. Semaglutide Effects on Heart Disease and Stroke in Patients With Overweight or Obesity (SELECT) ; Study Type: Interventional (Clinical Trial) ; Actual Enrollment We provide preliminary evidence that genotype-matched trial treatment selected on the basis of molecular profiling was associated with increased Selection of Trial Designs. Although there is no perfect all-encompassing Experimental designs for small randomised clinical trials: An algorithm for choice Counsel shall submit to the Special Master, forty-eight (48) hours prior to the selection of the jury, a joint statement or proposed special verdict questions Missing influence your selection of a mini-advisor. The history of mini-trials suggests that the negotiation period can be rocky, and the. To date, most neutrals Compact Trial Selection
Economical meal packages Compaft encountered in multi-arm Compact Trial Selection admits different extensions. There is a Free weight loss supplement samples amount Compact Trial Selection Tfial in rare diseases from observational studies. A mixed model Cmpact used to compare time on treatment, defined as the date of trial enrollment until the date of discontinuation of investigational treatment. Article CAS Google Scholar. In a trial with three treatment groups, assume that the investigator avoids the placebo treatment and equally favours the remaining treatment groups. Cluj Napoca, Cluj, Romania, S. Graf A, Bauer P, Glimm E et al. These therapies have been cited in the news media because of the extreme difficulty in recruiting participants into randomized trials of the therapies Altman, ; Brody, ; Kolata, , ; Kolata and Eichenwald, PubReader Print View Cite this Page Institute of Medicine US Committee on Strategies for Small-Number-Participant Clinical Research Trials; Evans CH Jr. In a trial with nonrandomized controls, the choice of intervention group and control group is decided deliberately. Azienda Osp-Univ Ferrara-Dip Scienze Mediche-Endocrinologia. For example, this could include people with a strong family history of cancer. Pediatric AIDS clinical trials group protocol study group. Semaglutide Effects on Heart Disease and Stroke in Patients With Overweight or Obesity (SELECT) ; Study Type: Interventional (Clinical Trial) ; Actual Enrollment We provide preliminary evidence that genotype-matched trial treatment selected on the basis of molecular profiling was associated with increased Selection of Trial Designs. Although there is no perfect all-encompassing Experimental designs for small randomised clinical trials: An algorithm for choice Multi-arm clinical trials have been gaining more and more importance, particularly due to the recent advances in small population group research [1]. Multi-arm This is the crux of the difficulty of selecting a randomisation method for small clinical trials. There is a tension between the two main Counsel shall submit to the Special Master, forty-eight (48) hours prior to the selection of the jury, a joint statement or proposed special verdict questions Compact Trial Selection

Compact Trial Selection - Missing Semaglutide Effects on Heart Disease and Stroke in Patients With Overweight or Obesity (SELECT) ; Study Type: Interventional (Clinical Trial) ; Actual Enrollment We provide preliminary evidence that genotype-matched trial treatment selected on the basis of molecular profiling was associated with increased Selection of Trial Designs. Although there is no perfect all-encompassing Experimental designs for small randomised clinical trials: An algorithm for choice

Integrative clinical sequencing in the management of refractory or relapsed cancer in youth. Roychowdhury S, Iyer MK, Robinson DR, Lonigro RJ, Wu Y-M, Cao X, et al. Personalized oncology through integrative high-throughput sequencing: a pilot study.

Sci Transl Med. Rodon J, Soria J, Berger R, Batist G, Tsimberidou A, Bresson C, et al. Challenges in initiating and conducting personalized cancer therapy trials: perspectives from WINTHER, a Worldwide Innovative Network WIN Consortium trial.

Ann Oncol. Sleijfer S, Bogaerts J, Siu LL. Designing transformative clinical trials in the cancer genome era. Gerlinger M, Rowan AJ, Horswell S, Larkin J, Endesfelder D, Gronroos E, et al.

Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. Download references. The authors acknowledge Swati Garg, PhD, and Mariam Thomas, PhD, Princess Margaret Cancer Centre, for their contributions to variant data analysis.

They are also thankful to the all of the medical oncologists, pathologists, laboratory technicians, clinical data coordinators, and correlative studies coordinators who participated in this research study.

This work was supported by the Princess Margaret Cancer Foundation; the Cancer Care Ontario Applied Clinical Research Unit [to LLS]; the University of Toronto Division of Medical Oncology Strategic Innovation [to PLB]; and the Ontario Ministry of Health and Long-Term Care Academic Health Sciences Centre Alternate Funding Plan Innovation Award [to PLB].

TLS and PLB had full access to all of the data in the study and take responsibility for the integrity of the data and accuracy of the data analysis. LLS, PLB, SK-R, and CY conceived of the study concept and wrote the protocol.

All authors participated in the acquisition, analysis, or interpretation of data. TS, SK-R, LLS, CY, and PLB drafted the manuscript for initial review by all authors. LW performed statistical analysis.

All authors read and approved the final manuscript. Laboratory Medicine Program, University Health Network, Toronto, Canada. Tracy L. Stockley, Hal K. Berman, Ming-Sound Tsao, Stefano Serra, Blaise Clarke, Michael H.

Roehrl, Tong Zhang, Mahadeo A. Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada. Cancer Genomics Program, Princess Margaret Cancer Centre, Toronto, Canada.

Stockley, Carl Virtanen, Raymond H. Kim, Celeste Yu, Trevor J. Pugh, Suzanne Kamel-Reid, Lillian L. Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University Avenue, Toronto, M5G 2M9, Canada. Amit M. Oza, Natasha B. Leighl, Jennifer J.

Knox, Frances A. Shepherd, Eric X. Chen, Monika K. Krzyzanowska, Neesha Dhani, Anthony M. Joshua, Raymond H. Kim, Albiruni R. Razak, Aaron R. Hansen, Lillian L. Department of Medicine, University of Toronto, Toronto, Canada. Department of Medical Biophysics, University of Toronto, Toronto, Canada.

Ming-Sound Tsao, Michael H. Roehrl, Trevor J. Department of Oncology, Grand River Regional Cancer Centre, Kitchener-Waterloo, Canada. Department of Oncology, McMaster University, Faculty of Health Sciences, Hamilton, Canada.

Department of Medicine, Markham Stouffville Hospital, Markham, Canada. Department of Biostatistics, Princess Margaret Cancer Centre, Toronto, Canada. Princess Margaret Research Institute, Princess Margaret Cancer Centre, Toronto, Canada.

You can also search for this author in PubMed Google Scholar. Correspondence to Philippe L. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. Reprints and permissions.

Stockley, T. et al. Genome Med 8 , Download citation. Received : 19 June Accepted : 11 October Published : 25 October Anyone you share the following link with will be able to read this content:.

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Download PDF. Stockley 1 , 2 , 3 , Amit M. Berman 1 , 2 , Natasha B. Leighl 4 , 5 , Jennifer J. Knox 4 , 5 , Frances A.

Shepherd 4 , 5 , Eric X. Chen 4 , 5 , Monika K. Krzyzanowska 4 , 5 , Neesha Dhani 4 , 5 , Anthony M. Joshua 4 , 5 , Ming-Sound Tsao 1 , 2 , 6 , Stefano Serra 1 , 2 , Blaise Clarke 1 , 2 , Michael H.

Roehrl 1 , 2 , 6 , Tong Zhang 1 , Mahadeo A. Sukhai 1 , Nadia Califaretti 7 , 8 , Mateya Trinkaus 9 , Patricia Shaw 1 , 2 , Theodorus van der Kwast 1 , 2 , Lisa Wang 10 , Carl Virtanen 3 , 11 , Raymond H. Razak 4 , 5 , Aaron R. Hansen 4 , 5 , Celeste Yu 3 , Trevor J.

Pugh 3 , 6 , 11 , Suzanne Kamel-Reid 1 , 2 , 3 , 6 , Lillian L. Bedard 3 , 4 , 5 Show authors Genome Medicine volume 8 , Article number: Cite this article 10k Accesses Citations 34 Altmetric Metrics details. Abstract Background The clinical utility of molecular profiling of tumor tissue to guide treatment of patients with advanced solid tumors is unknown.

Results From March to July , patients were enrolled and tested. Conclusions Few advanced solid tumor patients enrolled in a prospective institutional molecular profiling trial were treated subsequently on genotype-matched therapeutic trials.

Trial registration NCT date of registration 4 January Background Molecular profiling can provide diagnostic, prognostic, or treatment-related information to guide cancer patient management. Methods Patient cohort For IMPACT, patients with advanced solid tumors treated at PM were prospectively consented for molecular profiling during a routine clinical visit.

Specimens DNA was extracted from sections of FFPE tumor specimens from biopsies or surgical resections. Molecular profiling assays All testing was performed in a laboratory accredited by the College of American Pathologists CAP and certified to meet Clinical Laboratory Improvement Amendments CLIA.

Variant assessment and classification Variants were assessed and classified according to the classification scheme of Sukhai et al.

Return of testing results The molecular profiling report was included in the electronic medical record and returned to the treating oncologist. Clinical data collection For each patient, baseline patient and tumor characteristics, treatment regimen s , time on treatment s and survival were retrieved from medical records and updated every three months.

Statistics Descriptive statistics were used to summarize patient characteristics, profiling results, and anti-tumor activity. CONSORT diagram. Full size image. Table 2 Characteristics of patients enrolled in therapeutic trials following molecular profiling Full size table.

Discussion We demonstrated that molecular profiling with mass-spectrometry-based genotyping or targeted NGS can be implemented in a large academic cancer center to identify patients with advanced solid tumors who are candidates for genotype-matched clinical trials.

Conclusions We provide preliminary evidence that genotype-matched trial treatment selected on the basis of molecular profiling was associated with increased tumor shrinkage, although only a small proportion of profiled patients benefitted from this approach.

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Epub Nov Leite AR, Angelico-Goncalves A, Vasques-Novoa F, Borges-Canha M, Leite-Moreira A, Neves JS, Ferreira JP. Effect of glucagon-like peptide-1 receptor agonists on cardiovascular events in overweight or obese adults without diabetes: A meta-analysis of placebo-controlled randomized trials.

Diabetes Obes Metab. Epub May 3. Mares AC, Chatterjee S, Mukherjee D. Curr Opin Cardiol. Kanie T, Mizuno A, Takaoka Y, Suzuki T, Yoneoka D, Nishikawa Y, Tam WWS, Morze J, Rynkiewicz A, Xin Y, Wu O, Providencia R, Kwong JS. Dipeptidyl peptidase-4 inhibitors, glucagon-like peptide 1 receptor agonists and sodium-glucose co-transporter-2 inhibitors for people with cardiovascular disease: a network meta-analysis.

Cochrane Database Syst Rev. Layout table for MeSH terms Obesity Overweight Overnutrition Nutrition Disorders Body Weight. For Patients and Families For Researchers For Study Record Managers.

Home RSS Feeds Site Map Terms and Conditions Disclaimer Customer Support. Copyright Privacy Accessibility Viewers and Players Freedom of Information Act USA. gov HHS Vulnerability Disclosure U.

National Library of Medicine U. National Institutes of Health U. Department of Health and Human Services. The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Recruitment Status : Completed First Posted : July 2, Last Update Posted : January 23, Overweight Obesity.

Drug: Semaglutide Drug: Placebo semaglutide. Phase 3. Study Type :. Interventional Clinical Trial. Actual Enrollment :. Quadruple Participant, Care Provider, Investigator, Outcomes Assessor. Sponsor staff involved in the clinical trial is masked according to company standard procedures.

SELECT - Semaglutide Effects on Cardiovascular Outcomes in People With Overweight or Obesity. Actual Study Start Date :. Actual Primary Completion Date :. Actual Study Completion Date :. Experimental: Semaglutide Participants will receive semaglutide as an adjunct to standard-of-care.

Colorado Springs, Colorado, United States, Jacksonville Beach, Florida, United States, Arlington Heights, Illinois, United States, Oakbrook Terrace, Illinois, United States, Natchitoches, Louisiana, United States, Boston, Massachusetts, United States, East Brunswick, New Jersey, United States, Northwell Health-Division of Endocrinology Diabetes and Meta.

Saratoga Springs, New York, United States, Chapel Hill, North Carolina, United States, Greenville, North Carolina, United States, Wilmington, North Carolina, United States, Winston-Salem, North Carolina, United States, Danville, Pennsylvania, United States, Harleysville, Pennsylvania, United States, Philadelphia, Pennsylvania, United States, Charleston, South Carolina, United States, Greenville, South Carolina, United States, Mount Pleasant, South Carolina, United States, South Burlington, Vermont, United States, Wenatchee, Washington, United States, CHU Issad Hassani, Beni Messous, Cardiology department.

CHU - Hussein dey Cardiology department Nafissa Hamoud. Centro de Investigación y Prevención Cardiovascular.

Garran, Australian Capital Territory, Australia, Royal Adelaide Hospital Cardiovascular Clinical Trials. Elizabeth Vale, South Australia, Australia, Algemeen Stedelijk Ziekenhuis - Aalst - Interventional Cardiology.

Imeldaziekenhuis - Bonheiden - Department of Endocrinology. Uberlândia, Minas Gerais, Brazil, Passo Fundo, Rio Grande Do Sul, Brazil, CIP Centro Integrado de Pesquisas do Hospital de Base.

São José do Rio Preto, Sao Paulo, Brazil, Hospital do Coração Associação do Sanatório Sírio. Georgi Stranski" EAD, First Clinic of cardiology. of Cardiology. St-Marc-des-Carrières, Quebec, Canada, G0A 4B0.

IPS Centro Cientifico Asistencial Jose Luis Accini SAS. Unidad de Estudios Clínicos de la Fundación Cardiovascular de Colombia. Bucaramanga-piedecuesta, Valle Del Menzuli - Santander, Santander, Colombia, Fundacion del Caribe para la Investigacion Biomedica-BIOS.

Centro de Investigacion Clinica Avanzada y Multidisciplinari. CardiovidCentro Cardiovascular Colombiano ClinicaSantaMaria. Krapinske Toplice, Krapinsko Zagorska County, Croatia, Centre Hospitalier Universitaire de Dijon-Hopital Le Bocage.

Centre Hospitalier Universitaire Grenoble Alpes-Site Nord Michallon Centre Hospitalier Universitaire de Bordeaux-Hopital Haut Leveque Centre Hospitalier Universitaire de Nantes-Hopital Nord Laennec Les Hopitaux Universitaires de Strasbourg-Hopital Civil Hausaerztlich-Kardiologisches MVZ Am Felsenkeller GmbH.

Universitätsklinikum Leipzig, Endokrinologie und Nephrologie. RED-Institut für medizinische Forschung und Fortbildung GmbH. Zentrum für klinische Studien Allgäu Oberschwaben.

Forschungszentrum Ruhr KliFoCenter GmbH, Dr. Konstantopouleio G. of Athens, "Agia Olga". University General Hospital of Ioannina,Internal Medicine. Szegedi Tudomanyegyetem St Györgyi Albert Klinikai Központ. Szeged, Csongrád-Csanád, Hungary, H Debreceni Egyetem Klinikai Központ Belgyógyászati Klinika.

Medanta - The Medicity Multi-Speciality Hospital, Gurugram. Gandhi Memorial Hospital- King George's Medical University.

Diabetes and obesity center of excellence, Rambam MC. Institute of Endocrinology, metabolism and hypertension. Centro di Alta Spec. Cardio Metab. Campus Biomedico UOC Endocrinologia e Diabetologia. Università degli studi G. D'Annunzio Chieti Pescara - CAST.

Azienda Osp-Univ Ferrara-Dip Scienze Mediche-Endocrinologia. AOU Careggi Dipartimento Medico Geriatrico SOD Diabetologia. Azienda Ospedaliera Universitaria Federico II di Napoli. Unità Funzionale di nutrizione clinica cod IRCCS Fondazione "S.

Medicina Interna ed Endo. Azienda Ospedaliero Universitaria Pisana Ospedale Cisanello. UOC di Medicina Interna - Centro Medico dell'Obesità. Policlinico Universitario AGemelli DH Patologie dell'Obesità. Clinico Humanitas Endocrinologia e Malattie del ricambio.

Città della Salute e della Scienza di Torino. The Univ. of Cardiovascular Medicine. Ijinkai Takeda General Hospital, Cardiovascular Medicine. Medical Corporation Matsuyama-heartcenter Yotsuba Circulatio. Multi-arm trials can therefore be particularly susceptible to selection bias , a bias that can be introduced in a clinical trial due to heterogeneity of the patient population resulting from the predictability of the randomization sequence [ 6 ].

Even if a randomized trial is conducted double blind, selection bias may be introduced due to unmasking of past treatment assignments, for example due to side-effects. It interferes with the unbiased comparison of treatment effects that is the heart of each randomized controlled clinical trial.

Six decades ago, D. Blackwell and J. Hodges remarked [ 7 ]. It is widely recognized that experiments intended to compare two or more treatments may yield biased results if the experimental subjects are selected with knowledge of the treatments they are to receive.

Since then, the impact of selection bias in randomized clinical trials has been the subject of papers and guidelines [ 7 — 17 ]. Blackwell and Hodges [ 7 ] were the first to present a formal approach for quantifying selection bias.

Under the assumption that the investigator wishes to make one of the treatments appear better than the other, they presumed that the investigator would try to guess the treatment assignment for the next patient based on the knowledge of the past assignments.

For example, he would guess that a treatment is likely to be allocated next when it has so far been allocated less frequently. As a consequence, the investigator would include a patient with better prognosis always when his favoured treatment has currently been allocated less frequently in the trial.

A model for the guess of the investigator is called a guessing strategy. It has been shown to be an analogue to the degree of the predictability of a randomization sequence based on the allocation probabilities [ 6 ]. Strikingly, despite mentioning that selection bias is a problem also in multi-arm clinical trials, all of the mentioned sources focus on two-armed trials.

Some researchers may even feel that selection bias disappears when the number of treatment groups increases. In particular, no measure for selection bias in multi-arm randomized controlled clinical trials has been formally introduced.

Although Berger et al. Of all the measures that have been proposed for two-arm trials, the impact of selection bias on the type I error probability is most important from a regulatory point of view, as stated for example in the ICH E9 guideline [ 17 ].

In the present paper, we propose to measure selection bias in multi-arm trials by its influence on the test decision of the global F -test, when selection bias is modeled using a biasing policy , a generalization of the guessing strategy for two-arm trials proposed by Blackwell and Hodges [ 7 ] that models the heterogeneity in the patient stream due to selection bias.

The outline of the paper is as follows. There, we generalize the guessing strategy proposed by Blackwell and Hodges [ 7 ]. The variability encountered in multi-arm trials admits different extensions. We therefore present two generalized biasing policies that appear plausible in multi-arm trials from a practical point of view.

Then we derive the distribution of the F -statistic under the misspecified model and present a formula for the exact type I error probability conditional on a randomization sequence, followed by a numerical comparison of the impact of selection bias in multi-arm trials.

The supporting information contains R code for the computation of the presented formulae. Consider a randomized single center clinical trial without interim analyses. Assume patients are allocated using a K -arm parallel group design and balanced sample size per group and that the response is a continuous normal outcome.

To use formal notation, let the outcome y i of a patient i be the realization of a normally distributed random variable Y i with mean μ k if patient i is allocated to group k , and unknown variance σ 2.

Let N denote the total sample size and K the number of treatment groups. The matrix denotes the identity matrix of dimension N.

In what follows, we consider the null hypothesis that all group means are equal, 2 Under the normal assumption, this hypothesis is usually tested using an F -test with test statistic 3 where the matrix has all elements equal to one, and X t denotes the transpose of the design matrix X.

Obviously, the explicit form of the design matrix is a unique representation of the randomization list resulting from a particular randomization procedure. In the following, we restrict the consideration to fixed sample, non-adaptive, unstratified randomization procedures.

We focus our attention on the permuted block design PBD , the most commonly used randomization procedure for randomized controlled clinical trials with multiple treatment arms. Using the permuted block design, the patient stream is divided into M blocks.

This is a generalization of the blocked design using the notation of Berger et al. In case of PBD K , the design is balanced after every K th patient. In case of PBD N , we have one block of length N and balance is forced only at the end of the trial.

The design PBD N is also called random allocation rule and denoted by RAR. The restrictions imposed by the permuted block design introduce a certain predictability of the randomization sequence.

This predictability can lead to biased trial results. Already imperfect knowledge of the random assignments, e. when some past assignments were unmaksed due to side-effects, is sufficient to make future allocations predictable.

Formally, we will characterize predictability by the following two assumptions. Assumption 1. Assumption 2. In expectation the same number of patients is assigned to all treatment groups , namely. Based on these assumptions of predictability, Blackwell and Hodges [ 7 ] proposed to model the influence of selection bias on the expected responses in a two-arm trial.

They motivate their model by imagining an investigator who wishes to make one of the two treatments appear better than the other, even though the null hypothesis is true. They assume that the investigator, consciously or unconsciously, favours one treatment, say the experimental treatment.

If the investigator can guess that the next treatment to be assigned will be the experimental treatment, he might select a patient with higher expected response to be included in the trial. On the other hand, if he guesses the next assignment to be to the other treatment group, he might include a patient with worse expected response.

As a particular guessing strategy , it is sensible for the investigator to guess the treatment which at that point of the enrollment has been allocated less frequently, knowing that, in the end of the trial, the treatment groups are expected to be balanced.

Of course, the situation that an investigator guesses the next treatment assignments constitutes a worst case scenario. While Blackwell and Hodges [ 7 ] where concerned with the impact of selection bias on the mean difference between the treatment groups, we want to measure its impact in hypothesis tests with multi-arm trials.

In two-arm trials, Proschan [ 11 ] and Kennes et al. Proschan [ 11 ] coined the term biasing policy for the model of the biased patients responses. The generalization of the guessing strategy to multi-arm trials is not straight forward. On the one hand, an investigator might not strictly favour one treatment over all others, but might have a set of favoured treatments.

On the other hand, ties in the number of patients per treatment group will occur frequently, and there are several options of how to deal with them. In the following, we therefore propose two biasing policies that seem relevant from a practical point of view.

Different models for b arise depending on the guessing strategy of the investigator. The parameter is the strength of the shift introduced by the investigator.

We are interested in the effect of fitting the model described in Eq 1 , knowing that due to the misspecification that results from ignoring η b , the error term now follows a normal distribution with expectation η b and variance σ 2 I N.

To determine the components of b , a reasonable generalization of the Blackwell and Hodges model is that the investigator would favour a subset of treatment groups, and would assume that any of them will be assigned next, when all of the groups in have fewer patients than the remaining groups.

The investigator will guess that one of the not favoured groups will be allocated next, if all of the not favoured groups have fewer patients than the smallest of the favoured groups. The following example illustrates that the bias vector depends on the realization of the randomization sequence.

Example 1. In a trial with three treatment groups that compares one experimental treatment to two standard of care treatments, the investigator may adopt biasing policy I when he favours the experimental treatment as the favoured treatment,.

Table 1 shows the computation of the bias vector for the randomization list that is represented by the design matrix X with the columns x 1 , x 2 , x 3 shown in the table. We see that the first patient is allocated to group 1, the second to group 2, and so forth.

After including the first patient to the experimental group 1, group 1 is larger than any of the standard of care groups 2 and 3.

After the second patient, the experimental group 1 and the standard of care group 2 have the same number of patients, so the investigator is unsure which treatment will be assigned next, and includes a neutral patient.

An alternate bias model may result in a trial where several doses of an active treatment are compared to a placebo or a control treatment. In this situation the investigator may favour the active treatment, irrespective of the doses. He would try to allocate patients with lower expected response to the control groups, and patient with higher expected response to the experimental groups.

Following the same argument as above, the investigator would guess that one of his favoured treatment groups will be allocated next, when any of the groups in has fewer patients than any of the treatment groups , and guess the treatment groups when any treatment group in has more patients than the group of with fewest patients.

As before, the bias vector depends on the randomization sequence, as illustrated in the following example. Example 2. In a trial with three treatment groups, assume that the investigator avoids the placebo treatment and equally favours the remaining treatment groups.

Table 2 shows the computation of the bias vector for the design matrix X given by the columns x 1 , x 2 , x 3 shown in the table.

Note that the design matrix is the same as in Example 1, only the biasing policy changes. The first patient is allocated to the group 1 which is now the not favoured placebo group.

After the first allocation, the treatment group 3 is always smaller than the placebo group. Guessing that the next patient will be allocated to group 3, the investigator would include a patient with better expected response.

Examples 1 and 2 show that biasing policy I may introduce bias for fewer patients than biasing policy II, and can therefore be considered stricter. When applying the global F -test in the misspecified model given in Eq 1 , the type I error probability may be biased by the selection bias policy.

In order to measure the impact of selection bias on the test decision, we have to derive the distribution of the F -statistic S F in Eq 3 when selection bias is present. When the responses are influenced by selection bias which is defined by the bias vector b and depends on the randomization sequence, the error term in Eq 1 follows a normal distribution that is no longer identically distributed.

We now show that S F , the test statistic of the F -test, follows a doubly noncentral F -distribution. Using the notation 8 and definition Using Theorem 7. Third, using Theorem 7. This follows directly by multiplication.

From Eqs 9 and 10 it becomes clear that the noncentrality parameters, and therefore the distribution of the test statistic, depends on the particular realization of the randomization sequence.

Johnson et al. We further propose to consider the probability of an inflated type I error probability as evaluation criterion: 12 where P X denotes the probability of a randomization sequence represented by X , and Ω PBD denotes the set of all randomization sequences produced by PBD cK.

This section illustrates the use of the above derivations with numerical examples. We have shown that the rejection probability can be calculated for each individual randomization list generated by the a randomization procedure.

However, the number of sequences grows exponentially in N and K. Therefore, simulations are used for the calculation of the randomization lists, but not for the type I error probability. The derived distribution is represented by box plots and the corresponding summary statistic.

The R package randomizeR version 1. Then we calculate the distribution of the type I error probabilities as indicated in Eq 11 , and the proportion of sequences that lead to an inflated type I error probability as in Eq In doing so, we adopt a recommendation of Tamm et al.

In a first step, the above methodology is applied to investigate the difference between the biasing policies assuming the scenarios of Examples 1 and 2.

By Fegis

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