Level of evidence for promising subgroup findings: The case of trends and multiple subgroups. (13th March 2019)

• Record Type: Journal Article
• Authors: Tanniou, Julien; Smid, Sanne C.; van der Tweel, Ingeborg; Teerenstra, Steven; Roes, Kit C.B.
• Is Part Of: Statistics in medicine. Volume 38:Number 14(2019)
• Page Start: 2561
• Page End: 2572
• Publication Date: 2019-03-13
• Subjects: clinical trials -- failed study -- multiple testing -- overall nonsignificant trial -- subgroup analysis -- type I error; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.8133
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : Subgroup analyses are an essential part of fully understanding the complete results from confirmatory clinical trials. However, they come with substantial methodological challenges. In case no statistically significant overall treatment effect is found in a clinical trial, this does not necessarily indicate that no patients will benefit from treatment. Subgroup analyses could be conducted to investigate whether a treatment might still be beneficial for particular subgroups of patients. Assessment of the level of evidence associated with such subgroup findings is primordial as it may form the basis for performing a new clinical trial or even drawing the conclusion that a specific patient group could benefit from a new therapy. Previous research addressed the overall type I error and the power associated with a single subgroup finding for continuous outcomes and suitable replication strategies. The current study aims at investigating two scenarios as part of a nonconfirmatory strategy in a trial with dichotomous outcomes: (a) when a covariate of interest is represented by ordered subgroups, eg, in case of biomarkers, and thus, a trend can be studied that may reflect an underlying mechanism, and (b) when multiple covariates, and thus multiple subgroups, are investigated at the same time. Based on simulation studies, this paper assesses the credibility of subgroup findings in overall nonsignificant trials and provides practical recommendations for evaluating the strength of evidence of subgroup findings in these settings.