Sample size and power analysis in medical research.

ABSTRACT

Among the questions that a researcher should ask when planning a study is "How large a sample do I need?" If the sample size is too small, even a well conducted study may fail to answer its research question, may fail to detect important effects or associations, or may estimate those effects or associations too imprecisely. Similarly, if the sample size is too large, the study will be more difficult and costly, and may even lead to a loss in accuracy. Hence, optimum sample size is an essential component of any research. When the estimated sample size can not be included in a study, post-hoc power analysis should be carried out. Approaches for estimating sample size and performing power analysis depend primarily on the study design and the main outcome measure of the study. There are distinct approaches for calculating sample size for different study designs and different outcome measures. Additionally, there are also different procedures for calculating sample size for two approaches of drawing statistical inference from the study results, i.e. confidence interval approach and test of significance approach. This article describes some commonly used terms, which need to be specified for a formal sample size calculation. Examples for four procedures (use of formulae, readymade tables, nomograms, and computer software), which are conventionally used for calculating sample size, are also given.