TY - JOUR
T1 - Bayesian methods including nonrandomized study data increased the efficiency of postlaunch RCTs
AU - Schmidt, Amand F.
AU - Klugkist, Irene
AU - Klungel, Olaf H.
AU - Nielen, Mirjam
AU - de Boer, Anthonius
AU - Hoes, Arno W.
AU - Groenwold, Rolf H. H.
PY - 2015/4/1
Y1 - 2015/4/1
N2 - 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.
AB - 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.
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84925302257&origin=inward
UR - https://www.ncbi.nlm.nih.gov/pubmed/25554520
U2 - https://doi.org/10.1016/j.jclinepi.2014.11.015
DO - https://doi.org/10.1016/j.jclinepi.2014.11.015
M3 - Article
C2 - 25554520
SN - 0895-4356
VL - 68
SP - 387
EP - 396
JO - Journal of Clinical Epidemiology
JF - Journal of Clinical Epidemiology
IS - 4
ER -