1. Level of evidence for promising subgroup findings: The case of trends and multiple subgroups
- Author
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Steven Teerenstra, Julien Tanniou, Kit C.B. Roes, Sanne C. Smid, Ingeborg van der Tweel, CIC Brest, Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Hôpital de la Cavale Blanche, European Medicines Agency [London] (EMA), Julius Center for Health Sciences and Primary Care, University Medical Center [Utrecht], Department of Methodology and Statistic, Utrecht University [Utrecht], Medicines Evaluation Board [Utrecht], Radboud university [Nijmegen], Leerstoel Schoot, and Methodology and statistics for the behavioural and social sciences
- Subjects
Statistics and Probability ,medicine.medical_specialty ,failed study ,overall nonsignificant trial ,Epidemiology ,[SDV]Life Sciences [q-bio] ,Subgroup analysis ,01 natural sciences ,Healthcare improvement science Radboud Institute for Health Sciences [Radboudumc 18] ,010104 statistics & probability ,03 medical and health sciences ,All institutes and research themes of the Radboud University Medical Center ,0302 clinical medicine ,Bias ,Covariate ,Outcome Assessment, Health Care ,medicine ,030212 general & internal medicine ,0101 mathematics ,subgroup analysis ,ComputingMilieux_MISCELLANEOUS ,clinical trials ,Clinical Trials as Topic ,type I error ,business.industry ,Mechanism (biology) ,multiple testing ,Evidence-based medicine ,Clinical trial ,[STAT]Statistics [stat] ,Data Interpretation, Statistical ,Multiple comparisons problem ,business ,Clinical psychology ,Type I and type II errors - 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.
- Published
- 2019
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