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Applying propensity scores estimated in a full cohort to adjust for confounding in subgroup analyses
Author(s) -
Rassen Jeremy A.,
Glynn Robert J.,
Rothman Kenneth J.,
Setoguchi Soko,
Schneeweiss Sebastian
Publication year - 2012
Publication title -
pharmacoepidemiology and drug safety
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.023
H-Index - 96
eISSN - 1099-1557
pISSN - 1053-8569
DOI - 10.1002/pds.2256
Subject(s) - medicine , subgroup analysis , confounding , interquartile range , propensity score matching , cohort , population , cohort study , demography , statistics , confidence interval , mathematics , environmental health , sociology
Background A correctly specified propensity score (PS) estimated in a cohort (“cohort PS”) should, in expectation, remain valid in a subgroup population. Objective We sought to determine whether using a cohort PS can be validly applied to subgroup analyses and, thus, add efficiency to studies with many subgroups or restricted data. Methods In each of three cohort studies, we estimated a cohort PS, defined five subgroups, and then estimated subgroup‐specific PSs. We compared difference in treatment effect estimates for subgroup analyses adjusted by cohort PSs versus subgroup‐specific PSs. Then, over 10 million times, we simulated a population with known characteristics of confounding, subgroup size, treatment interactions, and treatment effect and again assessed difference in point estimates. Results We observed that point estimates in most subgroups were substantially similar with the two methods of adjustment. In simulations, the effect estimates differed by a median of 3.4% (interquartile (IQ) range 1.3–10.0%). The IQ range exceeded 10% only in cases where the subgroup had < 1000 patients or few outcome events. Conclusions Our empirical and simulation results indicated that using a cohort PS in subgroup analyses was a feasible approach, particularly in larger subgroups. Copyright © 2011 John Wiley & Sons, Ltd.