title: Propensity score matching and subclassification in observational studies with multi-level treatments.
abstract: In this article, we develop new methods for estimating average treatment effects in observational studies, in settings with more than two treatment levels, assuming unconfoundedness given pretreatment variables. We emphasize propensity score subclassification and matching methods which have been among the most popular methods in the binary treatment literature. Whereas the literature has suggested that these particular propensity-based methods do not naturally extend to the multi-level treatment case, we show, using the concept of weak unconfoundedness and the notion of the generalized propensity score, that adjusting for a scalar function of the pretreatment variables removes all biases associated with observed pretreatment variables. We apply the proposed methods to an analysis of the effect of treatments for fibromyalgia. We also carry out a simulation study to assess the finite sample performance of the methods relative to previously proposed methods.
authors.affiliation: Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts 02115, U.S.A.
authors.forename: Shu
authors.initials: S
authors.lastname: Yang
authors.affiliation: Graduate School of Business, Stanford University and NBER, Stanford, California 94305, U.S.A.
authors.forename: Guido W
authors.initials: GW
authors.lastname: Imbens
authors.affiliation: Real World Analytics, Eli Lilly and Company, Indianapolis, Indiana 46285, U.S.A.
authors.forename: Zhanglin
authors.initials: Z
authors.lastname: Cui
authors.affiliation: Real World Analytics, Eli Lilly and Company, Indianapolis, Indiana 46285, U.S.A.
authors.forename: Douglas E
authors.initials: DE
authors.lastname: Faries
authors.affiliation: Real World Analytics, Eli Lilly and Company, Vienna, Austria.
authors.forename: Zbigniew
authors.initials: Z
authors.lastname: Kadziola
mesh_terms: D015982:Bias; D003198:Computer Simulation; D005356:Fibromyalgia / Q000628:therapy; D006801:Humans; D015233:Models, Statistical; D064887:Observational Studies as Topic / Q000706:statistics & numerical data*; D057216:Propensity Score; D016896:Treatment Outcome
keywords: Generalized propensity score; Matching; Multi-level treatments; Potential outcomes; Subclassification; Unconfoundedness