title: The role of prediction modeling in propensity score estimation: an evaluation of logistic regression, bCART, and the covariate-balancing propensity score.
abstract: The covariate-balancing propensity score (CBPS) extends logistic regression to simultaneously optimize covariate balance and treatment prediction. Although the CBPS has been shown to perform well in certain settings, its performance has not been evaluated in settings specific to pharmacoepidemiology and large database research. In this study, we use both simulations and empirical data to compare the performance of the CBPS with logistic regression and boosted classification and regression trees. We simulated various degrees of model misspecification to evaluate the robustness of each propensity score (PS) estimation method. We then applied these methods to compare the effect of initiating glucagonlike peptide-1 agonists versus sulfonylureas on cardiovascular events and all-cause mortality in the US Medicare population in 2007-2009. In simulations, the CBPS was generally more robust in terms of balancing covariates and reducing bias compared with misspecified logistic PS models and boosted classification and regression trees. All PS estimation methods performed similarly in the empirical example. For settings common to pharmacoepidemiology, logistic regression with balance checks to assess model specification is a valid method for PS estimation, but it can require refitting multiple models until covariate balance is achieved. The CBPS is a promising method to improve the robustness of PS models.
authors.forename: Richard
authors.initials: R
authors.lastname: Wyss
authors.forename: Alan R
authors.initials: AR
authors.lastname: Ellis
authors.forename: M Alan
authors.initials: MA
authors.lastname: Brookhart
authors.forename: Cynthia J
authors.initials: CJ
authors.lastname: Girman
authors.forename: Michele
authors.initials: M
authors.lastname: Jonsson Funk
authors.forename: Robert
authors.initials: R
authors.lastname: LoCasale
authors.forename: Til
authors.initials: T
authors.lastname: Stürmer
mesh_terms: D015982:Bias; D002318:Cardiovascular Diseases / Q000453:epidemiology / Q000517:prevention & control; D015897:Comorbidity; D003198:Computer Simulation; D015986:Confounding Factors, Epidemiologic; D003920:Diabetes Mellitus / Q000188:drug therapy / Q000453:epidemiology; D052216:Glucagon-Like Peptide 1 / Q000819:agonists; D006801:Humans; D016013:Likelihood Functions; D016015:Logistic Models; D017891:Pharmacoepidemiology; D057216:Propensity Score; D013453:Sulfonylurea Compounds / Q000627:therapeutic use
chemical_list: D013453:Sulfonylurea Compounds; D052216:Glucagon-Like Peptide 1
keywords: cardiovascular disease; covariate balance; diabetes; epidemiologic methods; propensity score; regression; simulation