贝叶斯方法包括非随机研究数据，提高了上市后 RCT 的效率。
Bayesian methods including nonrandomized study data increased the efficiency of postlaunch RCTs.
机构信息
Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Universiteitsweg 100, P.O. Box 85500, 3508 GA Utrecht, The Netherlands; Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Universiteitsweg 99, P.O. Box 80082, 3508 TB Utrecht, The Netherlands; Department of Farm Animal Health, Faculty of Veterinary Medicine, Utrecht University, Yalelaan 7, 3584 CL Utrecht, The Netherlands; Institute of Cardiovascular Science, Faculty of Population Health, University College London, 222 Euston Road, London NW1 2DA, United Kingdom.
Department of Methodology and Statistics, Faculty of Social and Behavioural Sciences, Utrecht University, P.O. Box 80140, 3508 TC Utrecht, The Netherlands.
出版信息
J Clin Epidemiol. 2015 Apr;68(4):387-96. doi: 10.1016/j.jclinepi.2014.11.015. Epub 2014 Nov 28.
OBJECTIVES
Findings from nonrandomized studies on safety or efficacy of treatment in patient subgroups may trigger postlaunch randomized clinical trials (RCTs). In the analysis of such RCTs, results from nonrandomized studies are typically ignored. This study explores the trade-off between bias and power of Bayesian RCT analysis incorporating information from nonrandomized studies.
STUDY DESIGN AND SETTING
A simulation study was conducted to compare frequentist with Bayesian analyses using noninformative and informative priors in their ability to detect interaction effects. In simulated subgroups, the effect of a hypothetical treatment differed between subgroups (odds ratio 1.00 vs. 2.33). Simulations varied in sample size, proportions of the subgroups, and specification of the priors.
RESULTS
As expected, the results for the informative Bayesian analyses were more biased than those from the noninformative Bayesian analysis or frequentist analysis. However, because of a reduction in posterior variance, informative Bayesian analyses were generally more powerful to detect an effect. In scenarios where the informative priors were in the opposite direction of the RCT data, type 1 error rates could be 100% and power 0%.
CONCLUSION
Bayesian methods incorporating data from nonrandomized studies can meaningfully increase power of interaction tests in postlaunch RCTs.
目的
来自亚组患者治疗安全性或疗效的非随机研究结果可能会引发上市后随机临床试验（RCT）。在分析此类 RCT 时，通常会忽略非随机研究的结果。本研究探讨了在纳入非随机研究信息的贝叶斯 RCT 分析中，权衡偏差和功效的问题。
研究设计和设置
进行了一项模拟研究，比较了在其能力检测交互作用时，使用非信息先验和信息先验的频率派与贝叶斯分析。在模拟亚组中，假设治疗的效果在亚组之间存在差异（优势比 1.00 与 2.33）。模拟情况在样本量、亚组比例和先验指定方面存在差异。
结果
正如预期的那样，信息丰富的贝叶斯分析结果比非信息丰富的贝叶斯分析或频率派分析结果更具偏差。然而，由于后验方差的减少，信息丰富的贝叶斯分析通常更有能力检测到效果。在信息先验与 RCT 数据方向相反的情况下，1 型错误率可能为 100%，而功效为 0%。
结论
纳入非随机研究数据的贝叶斯方法可显著提高上市后 RCT 中交互检验的功效。
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