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Optimal matching approaches in health policy evaluations under rolling enrolment
Author(s) -
Pimentel Samuel D.,
Forrow Lauren Vollmer,
Gellar Jonathan,
Li Jiaqi
Publication year - 2020
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.12521
Subject(s) - outcome (game theory) , agnosticism , inference , selection (genetic algorithm) , matching (statistics) , randomization , causal inference , medicine , group (periodic table) , computer science , randomized controlled trial , econometrics , mathematics , statistics , artificial intelligence , mathematical economics , surgery , philosophy , epistemology , chemistry , organic chemistry
Summary Comparison group selection is paramount for health policy evaluations, where randomization is seldom practicable. Rolling enrolment is common in these evaluations, introducing challenges for comparison group selection and inference. We propose a novel framework, GroupMatch, for comparison group selection under rolling enrolment, founded on the notion of time agnosticism: two subjects with similar outcome trajectories but different enrolment periods may be more prognostically similar and produce better inference if matched, than two subjects with the same enrolment period but different pre‐enrolment trajectories. We articulate the conceptual advantages of this framework and demonstrate its efficacy in a simulation study and in an application to a study of the effect of falls in Medicare Advantage patients.