Semiparametric Estimation of Treatment Effect in a Pretest-Posttest Study with Missing Data.

Published

Journal Article

The pretest-posttest study is commonplace in numerous applications. Typically, subjects are randomized to two treatments, and response is measured at baseline, prior to intervention with the randomized treatment (pretest), and at prespecified follow-up time (posttest). Interest focuses on the effect of treatments on the change between mean baseline and follow-up response. Missing posttest response for some subjects is routine, and disregarding missing cases can lead to invalid inference. Despite the popularity of this design, a consensus on an appropriate analysis when no data are missing, let alone for taking into account missing follow-up, does not exist. Under a semiparametric perspective on the pretest-posttest model, in which limited distributional assumptions on pretest or posttest response are made, we show how the theory of Robins, Rotnitzky and Zhao may be used to characterize a class of consistent treatment effect estimators and to identify the efficient estimator in the class. We then describe how the theoretical results translate into practice. The development not only shows how a unified framework for inference in this setting emerges from the Robins, Rotnitzky and Zhao theory, but also provides a review and demonstration of the key aspects of this theory in a familiar context. The results are also relevant to the problem of comparing two treatment means with adjustment for baseline covariates.

Full Text

Duke Authors

Cited Authors

  • Davidian, M; Tsiatis, AA; Leon, S

Published Date

  • August 2005

Published In

Volume / Issue

  • 20 / 3

Start / End Page

  • 261 - 301

PubMed ID

  • 19081743

Pubmed Central ID

  • 19081743

International Standard Serial Number (ISSN)

  • 0883-4237

Digital Object Identifier (DOI)

  • 10.1214/088342305000000151

Language

  • eng

Conference Location

  • United States