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BOX 3-1

Some Statistical Approaches to Analysis of Small Clinical Trials

Sequential analysis

Hierarchical models

Bayesian analysis

Decision analysis

Statistical prediction

Meta-analysis and other alternatives

Risk-based allocation

data at hand and their context in comparison with those of other similar or related studies.

Since data analysis for small clinical trials inevitably involves a number of assumptions, it is logical that several different statistical analysis be conducted. If these analysis give consistent results under different assumptions, one can be more confident that the results are not due to unwarranted assumptions. In general, certain types of analysis (see Box 3-1) are more amenable to small studies. Each is briefly described in the sections that follow.


Sequential analysis refers to an analysis of the data as they accumulate, with a view toward stopping the study as soon as the results become statistically compelling. This is in contrast to a sequential design (see Chapter 2), in which the probability that a participant is assigned to a particular intervention is changed depending on the accumulating results. In sequential analysis the probabilty of assignment to an intervention is constant across the study.

Sequential analysis methods were first used in the context of industrial quality control in the late 1920s (Dodge and Romig, 1929). The use of sequential analysis in clinical trials has been extensively described by Armitage (1975), Heitjan (1997), and Whitehead (1999). Briefly, the data are analyzed as the results for each participant are obtained. After each observation, the decision is made to (1) continue the study by enrolling additional participants, (2) stop the study with the conclusion that there is a statistically significant difference between the treatments, or (3) stop the study and conclude that there is not a statistically significant difference between the

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