Biomaterial Database

Relative Performance of Propensity Score Matching Strategies for Subgroup Analyses. 2018

Shirley V Wang, and Yinzhu Jin, and Bruce Fireman, and Susan Gruber, and Mengdong He, and Richard Wyss, and HoJin Shin, and Yong Ma, and Stephine Keeton, and Sara Karami, and Jacqueline M Major, and Sebastian Schneeweiss, and Joshua J Gagne

Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.

Postapproval drug safety studies often use propensity scores (PSs) to adjust for a large number of baseline confounders. These studies may involve examining whether treatment safety varies across subgroups. There are many ways a PS could be used to adjust for confounding in subgroup analyses. These methods have trade-offs that are not well understood. We conducted a plasmode simulation to compare relative performance of 5 methods involving PS matching for subgroup analysis, including methods frequently used in applied literature whose performance has not been previously directly compared. These methods varied as to whether the overall PS, subgroup-specific PS, or no rematching was used in subgroup analysis as well as whether subgroups were fully nested within the main analytical cohort. The evaluated PS subgroup matching methods performed similarly in terms of balance, bias, and precision in 12 simulated scenarios varying size of the cohort, prevalence of exposure and outcome, strength of relationships between baseline covariates and exposure, the true effect within subgroups, and the degree of confounding within subgroups. Each had strengths and limitations with respect to other performance metrics that could inform choice of method.

UI	MeSH Term	Description	Entries
D008297	Male		Males
D008875	Middle Aged	An adult aged 45 - 64 years.	Middle Age
D011358	Product Surveillance, Postmarketing	Surveillance of drugs, devices, appliances, etc., for efficacy or adverse effects, after they have been released for general sale.	Drug Surveillance, Postmarketing,Evaluation Studies, Postmarketing,Postmarketing Product Surveillance,Product Surveillance, Post-Marketing,Drug Surveillances, Postmarketing,Evaluation Study, Postmarketing,Post-Marketing Product Surveillance,Post-Marketing Product Surveillances,Postmarketing Drug Surveillance,Postmarketing Drug Surveillances,Postmarketing Evaluation Studies,Postmarketing Evaluation Study,Postmarketing Product Surveillances,Product Surveillance, Post Marketing,Product Surveillances, Post-Marketing,Product Surveillances, Postmarketing,Studies, Postmarketing Evaluation,Study, Postmarketing Evaluation,Surveillance, Post-Marketing Product,Surveillance, Postmarketing Drug,Surveillance, Postmarketing Product,Surveillances, Post-Marketing Product,Surveillances, Postmarketing Drug,Surveillances, Postmarketing Product
D012107	Research Design	A plan for collecting and utilizing data so that desired information can be obtained with sufficient precision or so that an hypothesis can be tested properly.	Experimental Design,Data Adjustment,Data Reporting,Design, Experimental,Designs, Experimental,Error Sources,Experimental Designs,Matched Groups,Methodology, Research,Problem Formulation,Research Methodology,Research Proposal,Research Strategy,Research Technics,Research Techniques,Scoring Methods,Adjustment, Data,Adjustments, Data,Data Adjustments,Design, Research,Designs, Research,Error Source,Formulation, Problem,Formulations, Problem,Group, Matched,Groups, Matched,Matched Group,Method, Scoring,Methods, Scoring,Problem Formulations,Proposal, Research,Proposals, Research,Reporting, Data,Research Designs,Research Proposals,Research Strategies,Research Technic,Research Technique,Scoring Method,Source, Error,Sources, Error,Strategies, Research,Strategy, Research,Technic, Research,Technics, Research,Technique, Research,Techniques, Research
D003198	Computer Simulation	Computer-based representation of physical systems and phenomena such as chemical processes.	Computational Modeling,Computational Modelling,Computer Models,In silico Modeling,In silico Models,In silico Simulation,Models, Computer,Computerized Models,Computer Model,Computer Simulations,Computerized Model,In silico Model,Model, Computer,Model, Computerized,Model, In silico,Modeling, Computational,Modeling, In silico,Modelling, Computational,Simulation, Computer,Simulation, In silico,Simulations, Computer
D005260	Female		Females
D006801	Humans	Members of the species Homo sapiens.	Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000368	Aged	A person 65 years of age or older. For a person older than 79 years, AGED, 80 AND OVER is available.	Elderly
D000369	Aged, 80 and over	Persons 80 years of age and older.	Oldest Old
D000799	Angioedema	Swelling involving the deep DERMIS, subcutaneous, or submucosal tissues, representing localized EDEMA. Angioedema often occurs in the face, lips, tongue, and larynx.	Quincke's Edema,Urticaria, Giant,Angioneurotic Edema,Angioedemas,Angioneurotic Edemas,Edema, Angioneurotic,Edema, Quincke's,Edemas, Angioneurotic,Giant Urticaria,Giant Urticarias,Quincke Edema,Quinckes Edema,Urticarias, Giant