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Bayesian methods including nonrandomized study data increased the efficiency of postlaunch RCTs.

Abstract：

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.

journal_name

J Clin Epidemiol

journal_title

Journal of clinical epidemiology

authors

Schmidt AF,Klugkist I,Klungel OH,Nielen M,de Boer A,Hoes AW,Groenwold RH

doi

10.1016/j.jclinepi.2014.11.015

subject

Has Abstract

pub_date

2015-04-01 00:00:00

pages

387-96

issue

4

eissn

0895-4356

issn

1878-5921

pii

S0895-4356(14)00472-7

journal_volume

68

pub_type

杂志文章

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