A tutorial on propensity score estimation for multiple treatments using generalized boosted models.

ABSTRACT: The use of propensity scores to control for pretreatment imbalances on observed variables in non-randomized or observational studies examining the causal effects of treatments or interventions has become widespread over the past decade. For settings with two conditions of interest such as a treatment and a control, inverse probability of treatment weighted estimation with propensity scores estimated via boosted models has been shown in simulation studies to yield causal effect estimates with desirable properties. There are tools (e.g., the twang package in R) and guidance for implementing this method with two treatments. However, there is not such guidance for analyses of three or more treatments. The goals of this paper are twofold: (1) to provide step-by-step guidance for researchers who

SUBMITTER: McCaffrey DF 

PROVIDER: S-EPMC3710547 | biostudies-literature | 2013 Aug

REPOSITORIES: biostudies-literature