title: Optimally combining propensity score subclasses.
abstract: Propensity score methods, such as subclassification, are a common approach to control for confounding when estimating causal effects in non-randomized studies. Propensity score subclassification groups individuals into subclasses based on their propensity score values. Effect estimates are obtained within each subclass and then combined by weighting by the proportion of observations in each subclass. Combining subclass-specific estimates by weighting by the inverse variance is a promising alternative approach; a similar strategy is used in meta-analysis for its efficiency. We use simulation to compare performance of each of the two methods while varying (i) the number of subclasses, (ii) extent of propensity score overlap between the treatment and control groups (i.e., positivity), (iii) incorporation of survey weighting, and (iv) presence of heterogeneous treatment effects across subclasses. Both methods perform well in the absence of positivity violations and with a constant treatment effect with weighting by the inverse variance performing slightly better. Weighting by the proportion in subclass performs better in the presence of heterogeneous treatment effects across subclasses. We apply these methods to an illustrative example estimating the effect of living in a disadvantaged neighborhood on risk of past-year anxiety and depressive disorders among U.S. urban adolescents. This example entails practical positivity violations but no evidence of treatment effect heterogeneity. In this case, weighting by the inverse variance when combining across propensity score subclasses results in more efficient estimates that ultimately change inference. Copyright © 2016 John Wiley & Sons, Ltd.
authors.affiliation: School of Public Health, University of California, Berkeley. kara.rudolph@berkeley.edu.|Center for Health and Community, University of California, San Francisco. kara.rudolph@berkeley.edu.
authors.forename: Kara E
authors.initials: KE
authors.lastname: Rudolph
authors.affiliation: School of Public Health, University of California, Berkeley.
authors.forename: K Ellicott
authors.initials: KE
authors.lastname: Colson
authors.affiliation: Departments of Mental Health, Biostatistics, and Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.
authors.forename: Elizabeth A
authors.initials: EA
authors.lastname: Stuart
authors.affiliation: School of Public Health, University of California, Berkeley.
authors.forename: Jennifer
authors.initials: J
authors.lastname: Ahern
mesh_terms: D000293:Adolescent; D001007:Anxiety; D003863:Depression; D006801:Humans; D057216:Propensity Score; D011795:Surveys and Questionnaires
keywords: observational studies; propensity score; stratification; subclassification