Estimates of subgroup treatment effects in overall nonsignificant trials: To what extent should we believe in them?. (15th May 2017)

• Record Type: Journal Article
• Authors: Tanniou, Julien; van der Tweel, Ingeborg; Teerenstra, Steven; Roes, Kit C.B.
• Is Part Of: Pharmaceutical statistics. Volume 16:Number 4(2017)
• Page Start: 280
• Page End: 295
• Publication Date: 2017-05-15
• Subjects: bias -- clinical trials -- overall nonsignificant trial -- subgroup analysis; Pharmacy -- Statistical methods -- Periodicals; Pharmacy -- Statistics -- Periodicals; 615.10727
• DOI: 10.1002/pst.1810
• Languages: English
• ISSNs: 1539-1604

Abstract

Abstract : In drug development, it sometimes occurs that a new drug does not demonstrate effectiveness for the full study population but appears to be beneficial in a relevant subgroup. In case the subgroup of interest was not part of a confirmatory testing strategy, the inflation of the overall type I error is substantial and therefore such a subgroup analysis finding can only be seen as exploratory at best. To support such exploratory findings, an appropriate replication of the subgroup finding should be undertaken in a new trial. We should, however, be reasonably confident in the observed treatment effect size to be able to use this estimate in a replication trial in the subpopulation of interest. We were therefore interested in evaluating the bias of the estimate of the subgroup treatment effect, after selection based on significance for the subgroup in an overall "failed" trial. Different scenarios, involving continuous as well as dichotomous outcomes, were investigated via simulation studies. It is shown that the bias associated with subgroup findings in overall nonsignificant clinical trials is on average large and varies substantially across plausible scenarios. This renders the subgroup treatment estimate from the original trial of limited value to design the replication trial. An empirical Bayesian shrinkage method is suggested to minimize this overestimation. The proposed estimator appears to offer either a good or a conservative correction to the observed subgroup treatment effect hence provides a more reliable subgroup treatment effect estimate for adequate planning of future studies.