Small Clinical Trials Issues and Challenges (2001) / Chapter Skim
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2 Design of Small Clinical Trials
2 Design of Small Clinical Trials
Pages 20-59
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2 Design of Small Clinical Trials The design and conduct of any type of clinical trial require three considerations: first, the study should examine valuable and important biomedical research questions; second, it must be based on a rigorous methodology that can answer a specific research question being asked; and third, it must be based on a set of ethical considerations, adherence to which minimizes the risks to the study participants (Sutherland, Meslin, and Till, 19941. The choice of an appropriate study design depends on a number of considerations, including: tlon; · the ability of the study design to answer the primary research ques· whether the trial is studying a potential new treatment for a condition for which an established, effective treatment already exists; · whether the disease for which a new treatment is sought is severe or life-threatening; · the probability and magnitude of risk to the participants; · the probability and magnitude of likely benefit to the participants; · the population to be studied its size, availability, and accessibility; and · how the data will be used (e.g., to initiate treatment or as preliminary data for a larger trial)
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In general, the more controlled the trial, the stronger is the evidence. The study designs for clinical trials can take several forms, most of which are based on an assumption of accessible sample populations.
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These studies are carried out in the laboratory and in studies with animals to provide preliminary evidence that the experimental intervention will be safe and effective for humans. FDA requires preclinical testing before clinical trials can be started.
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Another device is the intraocular lens whose performance must be satisfied in a prespecified grid. Medical device studies, however, rely on a great deal of information about the behavior of the control group that often cannot be obtained or that is very difficult to obtain in small clinical trials because of the small number or lack of control participants.
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True equipoise might be more of a challenge in small clinical trials, because the degree of uncertainty might be diminished by the nature of the disorder, the lack of real choices for treatment, or insufficient data to make a judgment about the risks of one treatment arm over another. A primary purpose of many clinical trials is evaluation of the efficacy of an experimental intervention.
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Alternative Types of Control Groups A control group in a clinical trial is a group of individuals used as a comparison for a group of participants who receive the experimental treatment. The main purpose of a control group is to permit investigators to determine whether an observed effect is truly caused by the experimental intervention being tested or by other factors, such as the natural progression of the disease, observer or participant expectations, or other treatments (Pocock, 19961.
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Most placebo-controlled trials are also double blind, so that neither the participants nor the physician, investigator, or evaluator knows who is assigned to the placebo group and who will receive the experimental intervention. Placebo-controlled trials also allow a distinction between adverse events due to the intervention and those due to the underlying disease or other potential interference, if they occur sufficiently frequently to be detected with the available sample size.
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In a trial with no-treatment concurrent controls, a group receiving the experimental intervention is compared with a group not receiving the treatment or placebo. The randomized no-treatment control trial is similar to the placebo-controlled trial.
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, although one example has found the treatment effect to be underestimated (Farewell and D'Angio, 19811. 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.
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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.
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For example, nothing in the statistical output alerts the analyst to a potential nonoverlap in the marginal distributions. An investigator may be comparing 70-year-old smokers with 40-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.
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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.
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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.
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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 preferrecl.
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In these early stages it is sometimes necessary to compromise and study a somewhat less representative group (Pocock, 19841. Similarly, preliminary data collected from one population (e.g., from studies of bone mineral density loss in ground-based study participants)
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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.
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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.
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Moreover, the critical-effect size is individual- or population-specific as well as measurement-specific (Kraemer and Thiemann, 19871. TRADITIONAL CLINICAL TRIAL DESIGNS 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.
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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, 19841.
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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.
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Design 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, 19961.
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SPECIAL DESIGN ISSUES FOR SMALL TRIALS 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 2-31.
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· Pairs of treatment periods are replicated until the clinician and the patient are convinced that the experimental therapy is effective, is harmful, or has no effect on the treatment targets. 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, 19961.
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Is a blinded trial feasible? Is treatment effectiveness uncertain for the individual?
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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 e-of-1 designs, conclusions may be generalizable to the target population Johannessen, 1991; Zucker, Schmid, McIntosh, et al., 19971.
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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.
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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.
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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.
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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)
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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)
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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.
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The comparison group received the standard therapy at that time which omitted the bone marrow transplantation procedure. Bone marrow transplantation was widely available outside the clinical trial, and women were choosing that therapy in large numbers, drastically slowing patient enrollment in the trial.
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In a riskbased allocation trial, all of these high-risk women would have been given bone marrow transplantation, whereas women with fewer affected nodes would have been recruited and given the standard therapy. The treatment effect to be estimated in the assured allocation design would be the survival difference for women with at least 10 nodes given bone marrow transplantation compared with that for the same group of women if they had received the standard therapy.
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One need not rely on historical estimates of means or proportions of the expected outcome, which are notoriously untrustworthy. All one needs to assume for the risk-based design is that the mathematical form of the model relating outcome to risk is correctly specified throughout the entire range of the risk measure.
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That example uses a simple linear model to relate how much the level of total serum cholesterol was reduced from the baseline to the end of follow-up on the basis of a preliminary measurement of the cholesterol level among a group of cholesteremic, sedentary men in the placebo arm of a well-known randomized trial of the cholesterol-lowering compound cholestyramine. If the trial had been designed as a risk-based allocation trial, the actually observed lowering of the cholesterol level among the highestrisk (the most cholesteremic)
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An obvious risk factor that correlates with bone mineral density loss is the duration of the mission in space: the longer the mission, the greater the bone mineral density loss. What would be required in a risk-based study design is the mathematical form of this relationship for some standard countermeasures (countermeasure is the term that the National Aeronautics and Space Administration uses for a preventive or therapeutic intervention that mitigates bone mineral density loss or other physiological adaptations to longduration space travel)
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The committee strongly reaffirms that, whenever feasible, clinical trials should be designed and performed so that they have adequate statistical power. When the clinical context does not provide a sufficient number of research participants for a trial with adequate statistical power but the research question has great clinical significance, the committee understands that, by necessity for the advancement of human health, research will proceed.
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Careful consideration of alternative statistical design and analysis methods should occur at all stages in the multistep process of planning a clinical trial. When designing a small clinical trial, it is particularly important that the statistical design and analysis methods be customized to address the clinical research question and study population.
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There are two reasons for this: first, it allows the clinician to appropriately interpret the data within the clinical context, and second, it paves the way for meta-analysis with other small clinical trials or other future analyses of the study, for example, as part of a sequential design or meta-analysis. In the clinical setting, the consequences might be greater if one misinterprets the results.
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