Open AccessA permutation procedure to detect heterogeneous treatment effects in randomized clinical trials while controlling the type I error rate
Author(s) -
Jack M. Wolf,
Joseph S. Koopmeiners,
David M. Vock
Publication year - 2022
Publication title -
clinical trials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.559
H-Index - 63
eISSN - 1740-7753
pISSN - 1740-7745
DOI - 10.1177/17407745221095855
Subject(s) - type i and type ii errors , permutation (music) , word error rate , spurious relationship , population , computer science , randomized controlled trial , medicine , clinical trial , statistics , machine learning , mathematics , artificial intelligence , physics , environmental health , acoustics
Secondary analyses of randomized clinical trials often seek to identify subgroups with differential treatment effects. These discoveries can help guide individual treatment decisions based on patient characteristics and identify populations for which additional treatments are needed. Traditional analyses require researchers to pre-specify potential subgroups to reduce the risk of reporting spurious results. There is a need for methods that can detect such subgroups without a priori specification while allowing researchers to control the probability of falsely detecting heterogeneous subgroups when treatment effects are uniform across the study population.