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利用预试验为具有连续结果的干预试验设计提供信息的指南。

Guidance for using pilot studies to inform the design of intervention trials with continuous outcomes.

作者信息

Bell Melanie L, Whitehead Amy L, Julious Steven A

机构信息

Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA.

Medical Statistics Group, Design, Trials and Statistics, School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK.

出版信息

Clin Epidemiol. 2018 Jan 18;10:153-157. doi: 10.2147/CLEP.S146397. eCollection 2018.

Abstract

BACKGROUND

A pilot study can be an important step in the assessment of an intervention by providing information to design the future definitive trial. Pilot studies can be used to estimate the recruitment and retention rates and population variance and to provide preliminary evidence of efficacy potential. However, estimation is poor because pilot studies are small, so sensitivity analyses for the main trial's sample size calculations should be undertaken.

METHODS

We demonstrate how to carry out easy-to-perform sensitivity analysis for designing trials based on pilot data using an example. Furthermore, we introduce rules of thumb for the size of the pilot study so that the overall sample size, for both pilot and main trials, is minimized.

RESULTS

The example illustrates how sample size estimates for the main trial can alter dramatically by plausibly varying assumptions. Required sample size for 90% power varied from 392 to 692 depending on assumptions. Some scenarios were not feasible based on the pilot study recruitment and retention rates.

CONCLUSION

Pilot studies can be used to help design the main trial, but caution should be exercised. We recommend the use of sensitivity analyses to assess the robustness of the design assumptions for a main trial.

摘要

背景

一项预试验可以成为评估一项干预措施的重要一步,它能为设计未来的确定性试验提供信息。预试验可用于估计招募和保留率以及总体方差,并提供疗效潜力的初步证据。然而,由于预试验规模较小,估计结果不太准确,因此应该对主要试验的样本量计算进行敏感性分析。

方法

我们通过一个例子展示了如何基于预试验数据对设计试验进行易于执行的敏感性分析。此外,我们介绍了预试验规模的经验法则,以便将预试验和主要试验的总体样本量降至最低。

结果

该例子说明了通过合理改变假设,主要试验的样本量估计会如何大幅变化。根据假设,90%检验效能所需的样本量从392变化到692。基于预试验的招募和保留率,有些情况是不可行的。

结论

预试验可用于帮助设计主要试验,但应谨慎行事。我们建议使用敏感性分析来评估主要试验设计假设的稳健性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2de1/5779280/e5d35c165a3a/clep-10-153Fig1.jpg

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