title: On sample size estimation and re-estimation adjusting for variability in confirmatory trials.
abstract: Sample size estimation (SSE) is an important issue in the planning of clinical studies. While larger studies are likely to have sufficient power, it may be unethical to expose more patients than necessary to answer a scientific question. Budget considerations may also cause one to limit the study to an adequate size to answer the question at hand. Typically at the planning stage, a statistically based justification for sample size is provided. An effective sample size is usually planned under a pre-specified type I error rate, a desired power under a particular alternative and variability associated with the observations recorded. The nuisance parameter such as the variance is unknown in practice. Thus, information from a preliminary pilot study is often used to estimate the variance. However, calculating the sample size based on the estimated nuisance parameter may not be stable. Sample size re-estimation (SSR) at the interim analysis may provide an opportunity to re-evaluate the uncertainties using accrued data and continue the trial with an updated sample size. This article evaluates a proposed SSR method based on controlling the variability of nuisance parameter. A numerical study is used to assess the performance of proposed method with respect to the control of type I error. The proposed method and concepts could be extended to SSR approaches with respect to other criteria, such as maintaining effect size, achieving conditional power, and reaching a desired reproducibility probability.
authors.affiliation: a Department of Biostatistics and Bioinformatics , Duke University School of Medicine , Durham , North Carolina , USA.|b Center for Biologics Evaluation and Research , U.S. Food and Drug Administration , Silver Spring , Maryland , USA.
authors.forename: Pei-Shien
authors.initials: PS
authors.lastname: Wu
authors.affiliation: b Center for Biologics Evaluation and Research , U.S. Food and Drug Administration , Silver Spring , Maryland , USA.
authors.forename: Min
authors.initials: M
authors.lastname: Lin
authors.affiliation: a Department of Biostatistics and Bioinformatics , Duke University School of Medicine , Durham , North Carolina , USA.
authors.forename: Shein-Chung
authors.initials: SC
authors.lastname: Chow
mesh_terms: D000465:Algorithms; D002986:Clinical Trials as Topic / Q000191:economics / Q000706:statistics & numerical data*; D003627:Data Interpretation, Statistical; D006801:Humans; D012107:Research Design; D018401:Sample Size
keywords: Achieving conditional power; controlling variability; maintaining effect size; reproducibility probability