Exploiting multiple outcomes in Bayesian principal stratification analysis with application to the evaluation of a job training program
Journal Article (Journal Article)
The causal effect of a randomized job training program, the JOBS II study, on trainees' depression is evaluated. Principal stratification is used to deal with noncompliance to the assigned treatment. Due to the latent nature of the principal strata, strong structural assumptions are often invoked to identify principal causal effects. Alternatively, distributional assumptions may be invoked using a model-based approach. These often lead to weakly identified models with substantial regions of flatness in the posterior distribution of the causal effects. Information on multiple outcomes is routinely collected in practice, but is rarely used to improve inference. This article develops a Bayesian approach to exploit multivariate outcomes to sharpen inferences in weakly identified principal stratification models. We show that inference for the causal effect on depression is significantly improved by using the reemployment status as a secondary outcome in the JOBS II study. Simulation studies are also performed to illustrate the potential gains in the estimation of principal causal effects from jointly modeling more than one outcome. This approach can also be used to assess plausibility of structural assumptions and sensitivity to deviations from these structural assumptions. Two model checking procedures via posterior predictive checks are also discussed. © Institute of Mathematical Statistics, 2013.
Full Text
Duke Authors
Cited Authors
- Mattei, A; Li, F; Mealli, F
Published Date
- December 1, 2013
Published In
Volume / Issue
- 7 / 4
Start / End Page
- 2336 - 2360
Electronic International Standard Serial Number (EISSN)
- 1941-7330
International Standard Serial Number (ISSN)
- 1932-6157
Digital Object Identifier (DOI)
- 10.1214/13-AOAS674
Citation Source
- Scopus