Improved stopping rules for the design of efficient small-sample experiments in biomedical and biobehavioral research

摘要

Sequential stopping rules (SSRs) should augment traditional hypothesis tests in many planned experiments, because they can provide the same statistical power with up to 30% fewer subjects without additional education or software. This article includes new Monte-Carlo-generated power curves and tables of stopping criteria based on the p values from simulated t tests and one-way ANOVAs. The tables improve existing SSR techniques by holding alpha very close to a target value when 1–10 subjects are added at each iteration. The emphasis is on small sample sizes (3–40 subjects per group) and large standardized effect sizes (0.8–2.0). The generality of the tables for dependent samples and one-tailed tests is discussed. SSR methods should be of interest to ethics bodies governing research when it is desirable to limit the number of subjects tested, such as in studies of pain, experimental disease, or surgery with animal or human subjects.