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A review of the performance of different methods for propensity score matched subgroup analyses and a summary of their application in peer‐reviewed research studies
Author(s) -
Wang Shirley V.,
He Mengdong,
Jin Yinzhu,
Wyss Richard,
Shin HoJin,
Ma Yong,
Keeton Stephine,
Fireman Bruce,
Karami Sara,
Major Jacqueline M.,
Schneeweiss Sebastian,
Gagne Joshua J.
Publication year - 2017
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.4328
Subject(s) - propensity score matching , subgroup analysis , medicine , covariate , matching (statistics) , statistics , econometrics , meta analysis , mathematics , pathology
Purpose When evaluating safety signals, there is often interest in understanding safety in all patients for whom compared treatments are reasonable alternatives, as well as in specific subgroups of interest. There are numerous ways that propensity score (PS) matching can be implemented for subgroup analyses. Methods We conducted a systematic literature review of methods papers that compared the performance of alternative methods to implement PS matched subgroup analyses and examined how frequently different PS matching methods have been used for subgroup analyses in applied studies. Results We identified 5 methods papers reporting small improvements in covariate balance and bias with use of a subgroup‐specific PS instead of a mis‐specified overall PS within subgroups. Applied research papers frequently used PS for subgroups in ways not evaluated in methods papers. Thirty three percent used PS to match in the overall cohort and broke the matched sets for subgroup analysis without further adjustment. Conclusions While the performance of several alternative ways to use PS matching in subgroup analyses has been evaluated in methods literature, these evaluations do not include the most commonly used methods to implement PS matched subgroup analyses in applied studies. There is a need to better understand the relative performance of commonly used methods for PS matching in subgroup analyses, particularly within settings encountered during active surveillance, where there may be low exposure, infrequent outcomes, and multiple subgroups of interest.