摘要：I reviewed sample estimation methods for research designs involving nonindependent data and a dichotomous response variable to examine the importance of proper sample size estimation and the need to align methods of sample size estimation with planned methods of statistical analysis. Examples and references to published literature are provided in this article. When the method of sample size estimation is not in concert with the method of planned analysis, poor estimates may result. The effects of multiple measures over time also need to be considered. Proper sample size estimation is often overlooked. Alignment of the sample size estimation method with the planned analysis method, especially in studies involving nonindependent data, will produce appropriate estimates. WHEN DESIGNING A STUDY— whether a program evaluation, a survey, a case–control comparison, or a clinical trial—investigators often overlook sample size estimation. For ethical and practical reasons, it is important to accurately estimate the required sample size when one is testing a hypothesis or estimating the size of an effect in observational research. 1– 3 I seek to advance the existing literature by examining 3 points: (1) the importance of sample size estimation in research, (2) the need for alignment of sample size estimation with the planned analysis, and (3) the special case of a design involving clustered or correlated data and a dichotomous outcome. This discussion is framed primarily in terms of longitudinal study designs, which are more common and probably more familiar to many researchers than cluster-randomized designs. The broader points, however, apply to all research settings in which sample size is important. The more specific issues and methods apply to any design in which the data are nonindependent, such as studies of members of a household, comparisons of entire communities, and multiple measures of the same person. This topic can be framed from 2 separate perspectives: testing hypotheses and estimating parameters. When testing a hypothesis, one is concerned with estimating the number of study participants required to ensure a minimal probability (power) of detecting an effect if it exists. With many public health applications, the goal is not to test a hypothesis but rather to estimate the size of an effect, such as an odds ratio, a correlation coefficient, or a proportion. The focus is on the variation of the estimate, which is expressed by the size of the confidence interval after one asks the question, “If I have a sample of a given size, how large will the confidence interval around my estimate be?” Proper sample size estimation is equally important in both perspectives.
