Thus, analyses of data must consider the limitations of the A single large clinical trial is often insufficient to answer a biomedical research question, and it is even more unlikely that a single small clinical trial can do so. Instead, it might be necessary to summarize all of the evidence from the trial and combine it with other evidence available from other trials or laboratory studies. This is because a small clinical trial is less likely to be self-contained, providing all of the necessary evidence to effectively test a particular hypothesis. Thus, in some cases it might be important to focus on evidence rather than to test a hypothesis (Royall, 1997). Thus, testing of a null hypothesis might be particularly challenging in the context of a small clinical trial. It may be that statistical hypothesis testing is premature. When the sample population is small, it is important to gather considerable preliminary evidence on related subjects before the trial is conducted to define the size needed to determine a critical effect. 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. Data analysis for small clinical trials in particular must be focused. Assuming that a clinical trial will produce data that could reveal differences in effects between two or more interventions, statistical analyses are used to determine whether such differences are real or are due to chance. 3 Statistical Approaches to Analysis of Small ClinicalTrialsĪ necessary companion to well-designed clinical trial is its appropriate statistical analysis.
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