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Beyond generalization of the ATE: Designing randomized trials to understand treatment effect heterogeneity
Author(s) -
Tipton Elizabeth
Publication year - 2021
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/rssa.12629
Subject(s) - moderation , randomized controlled trial , treatment effect , population , selection (genetic algorithm) , average treatment effect , generalization , econometrics , randomized experiment , focus (optics) , psychology , computer science , economics , medicine , statistics , mathematics , machine learning , social psychology , propensity score matching , mathematical analysis , physics , surgery , environmental health , optics , traditional medicine
Researchers conducting randomized trials have increasingly shifted focus from the average treatment effect to understanding moderators of treatment effects. Current methods for exploring moderation focus on model selection and hypothesis tests. At the same time, recent developments in the design of randomized trials have argued for the need for population‐based recruitment in order to generalize well. In this paper, we show that a different population‐based recruitment strategy can be implemented to increase the precision of estimates of treatment effect moderators, and we explore the trade‐offs between optimal designs for the average treatment effect and moderator effects.