title: Model feedback in Bayesian propensity score estimation.
abstract: Methods based on the propensity score comprise one set of valuable tools for comparative effectiveness research and for estimating causal effects more generally. These methods typically consist of two distinct stages: (1) a propensity score stage where a model is fit to predict the propensity to receive treatment (the propensity score), and (2) an outcome stage where responses are compared in treated and untreated units having similar values of the estimated propensity score. Traditional techniques conduct estimation in these two stages separately; estimates from the first stage are treated as fixed and known for use in the second stage. Bayesian methods have natural appeal in these settings because separate likelihoods for the two stages can be combined into a single joint likelihood, with estimation of the two stages carried out simultaneously. One key feature of joint estimation in this context is "feedback" between the outcome stage and the propensity score stage, meaning that quantities in a model for the outcome contribute information to posterior distributions of quantities in the model for the propensity score. We provide a rigorous assessment of Bayesian propensity score estimation to show that model feedback can produce poor estimates of causal effects absent strategies that augment propensity score adjustment with adjustment for individual covariates. We illustrate this phenomenon with a simulation study and with a comparative effectiveness investigation of carotid artery stenting versus carotid endarterectomy among 123,286 Medicare beneficiaries hospitlized for stroke in 2006 and 2007.
authors: Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, USA. czigler@hsph.harvard.edu
authors: Corwin M
authors: CM
authors: Zigler
authors: Krista
authors: K
authors: Watts
authors: Robert W
authors: RW
authors: Yeh
authors: Yun
authors: Y
authors: Wang
authors: Brent A
authors: BA
authors: Coull
authors: Francesca
authors: F
authors: Dominici
mesh_terms: D000368:Aged; D001499:Bayes Theorem; D016893:Carotid Stenosis; D003198:Computer Simulation; D016894:Endarterectomy, Carotid / Q000592:standards; D005246:Feedback; D005260:Female; D006801:Humans; D008297:Male; D015233:Models, Statistical; D057216:Propensity Score; D020521:Stroke / Q000628:therapy
references: Stat Med. 2007 Jan 15;26(1):20-36
references: 17072897
references: Stat Med. 2009 Jan 15;28(1):94-112
references: 19012268
references: Biometrics. 2005 Dec;61(4):962-73
references: 16401269
references: Biometrics. 2012 Sep;68(3):675-8; discussion 680-6
references: 22348262
references: Stroke. 2009 Nov;40(11):3574-9
references: 19797179
references: Epidemiology. 2009 Jul;20(4):512-22
references: 19487948
references: Biometrics. 2012 Sep;68(3):661-71
references: 22364439
references: Health Care Financ Rev. 2004 Summer;25(4):119-41
references: 15493448
references: Int J Biostat. 2010;6(2):Article 16
references: 21972431
references: Biometrics. 2012 Sep;68(3):678-80; discussion 680-6
references: 22348234
references: Stat Sci. 2010 Feb 1;25(1):1-21
references: 20871802