title: Comparison of Propensity Score Methods and Covariate Adjustment: Evaluation in 4 Cardiovascular Studies.
abstract: Propensity scores (PS) are an increasingly popular method to adjust for confounding in observational studies. Propensity score methods have theoretical advantages over conventional covariate adjustment, but their relative performance in real-word scenarios is poorly characterized. We used datasets from 4 large-scale cardiovascular observational studies (PROMETHEUS, ADAPT-DES [the Assessment of Dual AntiPlatelet Therapy with Drug-Eluting Stents], THIN [The Health Improvement Network], and CHARM [Candesartan in Heart Failure-Assessment of Reduction in Mortality and Morbidity]) to compare the performance of conventional covariate adjustment with 4 common PS methods: matching, stratification, inverse probability weighting, and use of PS as a covariate. We found that stratification performed poorly with few outcome events, and inverse probability weighting gave imprecise estimates of treatment effect and undue influence to a small number of observations when substantial confounding was present. Covariate adjustment and matching performed well in all of our examples, although matching tended to give less precise estimates in some cases. PS methods are not necessarily superior to conventional covariate adjustment, and care should be taken to select the most suitable method.
authors.affiliation: Department of Biostatistics, London School of Hygiene and Tropical Medicine, London, United Kingdom; Innovative Pediatric Oncology Drug Development, F. Hoffmann-La Roche AG, Basel, Switzerland.
authors.forename: Markus C
authors.initials: MC
authors.lastname: Elze
authors.affiliation: Department of Biostatistics, London School of Hygiene and Tropical Medicine, London, United Kingdom.
authors.forename: John
authors.initials: J
authors.lastname: Gregson
authors.affiliation: Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York.
authors.forename: Usman
authors.initials: U
authors.lastname: Baber
authors.affiliation: Department of Biostatistics, London School of Hygiene and Tropical Medicine, London, United Kingdom.
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Williamson
authors.affiliation: Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York.
authors.forename: Samantha
authors.initials: S
authors.lastname: Sartori
authors.affiliation: Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York.
authors.forename: Roxana
authors.initials: R
authors.lastname: Mehran
authors.affiliation: Division of Cardiology, Columbia University Medical Center, New York-Presbyterian Hospital, New York, New York; Cardiovascular Research Foundation, New York, New York.
authors.forename: Melissa
authors.initials: M
authors.lastname: Nichols
authors.affiliation: Division of Cardiology, Columbia University Medical Center, New York-Presbyterian Hospital, New York, New York; Cardiovascular Research Foundation, New York, New York.
authors.forename: Gregg W
authors.initials: GW
authors.lastname: Stone
authors.affiliation: Department of Biostatistics, London School of Hygiene and Tropical Medicine, London, United Kingdom. Electronic address: stuart.pocock@lshtm.ac.uk.
authors.forename: Stuart J
authors.initials: SJ
authors.lastname: Pocock
mesh_terms: D002318:Cardiovascular Diseases / Q000503:physiopathology*; D006801:Humans; D011336:Probability; D057216:Propensity Score
keywords: bias; comparison of methods; observational studies; regression