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3 Statistical Approaches to Analysis of Small Clinical Trials
Pages 60-90

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From page 60...
... Data analysis for small clinical trials in particular must be focused. In the context of a small clinical trial, it is especially important for researchers to make a clear distinction between preliminary evidence and confirmatory data analysis.
From page 61...
... Sequential analysis methods were first used in the context of industrial quality control in the late 1920s (Dodge and Romig, 19291. The use of sequential analysis in clinical trials has been extensively described by Armitage (1975)
From page 62...
... For example, take the case study of sickle cell disease introduced in Chapter 1 and consider the analysis of the clinical design problem introduced in Box 1-4 as an example of sequential analysis (Box 3-21. Data from a clinical trial accumulate gradually over a period of time that can extend to months or even years.
From page 64...
... rules that are based on successive examinations of accumulating data may cause difficulties because of the need to reconcile such stopping rules with the standard approach to statistical analysis used for the analysis of data from most clinical trials. This standard approach is known as the "frequentist approach." In this approach the analysis takes a form that is dependent on the study design.
From page 65...
... B Establishment of repeated confidence intervals for a clinical intervention for prevention of loss of bone mineral density for determination of the success (S)
From page 66...
... For example, in an experiment where the primary response variable is quantitative, the sample size is often set assuming this variable to be normally distributed with a certain variance. For binary response data, sample size calculations rely on an assumed value for the background incidence rate; for time-to-event data when individuals enter the trial at staggered intervals, an estimate of the subject accrual rate is important in determining the appropriate accrual period.
From page 67...
... First, hierarchical models provide a natural framework for combining information from a series of small clinical trials conducted
From page 68...
... Second, hierarchical models also provide a foundation for analysis of longitudinal studies, which are necessary for increasing the power of research involving small clinical trials. By repeatedly obtaining data for the same subject over time as part of a study of a single treatment or a crossover study, the total number of subjects required in the trial is reduced.
From page 69...
... on bone mineral density measurements taken at multiple points in time during the course of a space mission. At the end of the study, the data comprise a file of bone mineral density measurements for each patient (astronaut)
From page 70...
... Note that hierarchical models are equally useful in the context of clustered data, in which participants are nested within groups (e.g., different studies or space missions) , and the sharing of this similar environment induces a correlation among the responses of participants within strata.
From page 71...
... Some statistical details of the general linear hierarchical regression model are provided in Appendix A The case study presented in Box 3-4 provides an example of how hierarchical models can be used to aid in the design and analysis of small clinical trials.
From page 72...
... Its flexibility ancl lack of concern for interim inspections are especially valuable in sequential clinical trials. The main problem with the Bayesian approach, however, lies in the idea of a ..
From page 73...
... Bayesian hierarchical models provide a natural framework for combining information from different sources. This is often referred to as "meta-analysis" in the context of clinical trials, but the methods are quite broadly applicable.
From page 74...
... The other major advantage of decision analysis occurs after data collection. If one assumes that the sample size is inadequate and therefore that the confidence intervals on the effect in question are wide, one may still have a clinical situation for which a decision is required.
From page 75...
... (15) = 13 FIGURE 3-2 Decision analysis expectation.
From page 76...
... - stmSP -- stmFx 1 - p(1 - e) ~ ~ L¢'- stmSP No Fracture Fracture __~ r ~ ~ p t~qrxJ - stmrx Routine Program ~ ~ P HE No Fracture FIGURE 3-3 Decision tree for preventing osteoporotic fractures in space.
From page 77...
... Conceptualized in that way, the problem is one of deriving a limit or interval on the basis of the control distribution that will include the mean or median for all or a subset of the experimental cluster samples. For example, one may wish to compare the median bone mineral density loss in 5 astronauts in each of five future space missions (i.e., a total of 25 astronauts clustered in groups of 5 each)
From page 78...
... may be obtained. The net result is that a much smaller prediction limit can be used sequentially compared with the limit that would be used if the statistical prediction decision was based on the result of a single comparison, leading to a dra
From page 79...
... . This idea can be directly adapted to the problem of loss of bone mineral density in astronauts, particularly with respect to the design and analysis of data from a series of small clinical trials (e.g., space missions, each consisting of a small number of astronauts)
From page 80...
... The method involves characterization of the distribution of control measurements and the use of parameters for the control distribution to draw inferences from a series of more limited samples of experimental measurements. This is a classical problem in statistical prediction and departs from the more commonly used paradigm of hypothesis testing.
From page 82...
... TABLE 3-2 Key Points in the Concluct of Meta-Analyses Systematic reviews of study findings often use complex statistical methods to synthesize and interpret data from individual studies, and an understanding of their basic principles is important in interpreting their results. Quantitative synthesis cannot replace sound clinical reasoning; combining poorquality or overly biased data that do not make sense is likely to produce unreliable results.
From page 83...
... When the small studies are replicates of each other as, for example, in collaborative laboratory or clinical studies
From page 84...
... The CPM analysis approach differs from other meta-analysis techniques based on classical statistics in that it provides marginal probability distributions for the parameters of interest and if an integrated approach is used, a joint probability distribution for all the parameters. More common metaanalysis procedures provide a point estimate for one or more effect sizes together with confidence intervals for the estimates.
From page 85...
... Because those who perform meta-analyses typically weight the results in proportion to sample size, small sample sizes have less of an effect on the results than larger ones. A synthesis based mainly on small sample sizes will produce summary results with more uncertainty (larger standard errors and wider confidence intervals)
From page 86...
... Cumulative meta-analysis can help determine when additional studies are needed and can improve the predictability of previous small trials (Villar, Carroll, and Belizan, 19951. Several workshops have produced a set of guidelines for the reporting of meta-analysis of randomized clinical trials (the Quality of Reporting of Meta-Analysis group statement IMoher, Cook, Eastwood, et al., 19991, the Consolidated Standard of Reporting Trials conference statement IBegg, Cho, Eastwood, et al., 19961, and the Meta-Analysis of Observational Studies in Epidemiology group statement on meta-analysis of observational studies I Stroup, Berlin, Morton, et al., 200011.
From page 87...
... Small clinical trials frequently need to be viewed as part of a process of continuing data collection; thus, the objectives of a small clinical trial should be understood in that context. For example, a small clinical trial often guides the design of a subsequent trial.
From page 89...
... Given the greater uncertainties inherent in small clinical trials, several alternative statistical analyses should be performed to evaluate the consistency and robustness of the results of a small clinical trial. The use of alternative statistical analyses might help identify the more sensitive variables and the key interactions in applying heterogeneous results across trials or in trying to make generalizations across trials.
From page 90...
... RECOMMENDATION: Exercise caution in interpretation. One should exercise caution in the interpretation of the results of small clinical trials before attempting to extrapolate or generalize those results.


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