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Guidelines for Designing and Evaluating Feasibility Pilot Studies

样本量测定 计算机科学 统计能力 非参数统计 研究设计 忠诚 实验设计 统计 医学物理学 医学 数学 电信
作者
Jeanne A. Teresi,Xiaoying Yu,Anita L. Stewart,Ron D. Hays
出处
期刊:Medical Care [Lippincott Williams & Wilkins]
卷期号:60 (1): 95-103 被引量:218
标识
DOI:10.1097/mlr.0000000000001664
摘要

Pilot studies test the feasibility of methods and procedures to be used in larger-scale studies. Although numerous articles describe guidelines for the conduct of pilot studies, few have included specific feasibility indicators or strategies for evaluating multiple aspects of feasibility. In addition, using pilot studies to estimate effect sizes to plan sample sizes for subsequent randomized controlled trials has been challenged; however, there has been little consensus on alternative strategies.In Section 1, specific indicators (recruitment, retention, intervention fidelity, acceptability, adherence, and engagement) are presented for feasibility assessment of data collection methods and intervention implementation. Section 1 also highlights the importance of examining feasibility when adapting an intervention tested in mainstream populations to a new more diverse group. In Section 2, statistical and design issues are presented, including sample sizes for pilot studies, estimates of minimally important differences, design effects, confidence intervals (CI) and nonparametric statistics. An in-depth treatment of the limits of effect size estimation as well as process variables is presented. Tables showing CI around parameters are provided. With small samples, effect size, completion and adherence rate estimates will have large CI.This commentary offers examples of indicators for evaluating feasibility, and of the limits of effect size estimation in pilot studies. As demonstrated, most pilot studies should not be used to estimate effect sizes, provide power calculations for statistical tests or perform exploratory analyses of efficacy. It is hoped that these guidelines will be useful to those planning pilot/feasibility studies before a larger-scale study.
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