Covariate adjustment for two-sample treatment comparisons in randomized clinical trials: a principled yet flexible approach.

Journal Article (Journal Article)

There is considerable debate regarding whether and how covariate-adjusted analyses should be used in the comparison of treatments in randomized clinical trials. Substantial baseline covariate information is routinely collected in such trials, and one goal of adjustment is to exploit covariates associated with outcome to increase precision of estimation of the treatment effect. However, concerns are routinely raised over the potential for bias when the covariates used are selected post hoc and the potential for adjustment based on a model of the relationship between outcome, covariates, and treatment to invite a 'fishing expedition' for that leading to the most dramatic effect estimate. By appealing to the theory of semiparametrics, we are led naturally to a characterization of all treatment effect estimators and to principled, practically feasible methods for covariate adjustment that yield the desired gains in efficiency and that allow covariate relationships to be identified and exploited while circumventing the usual concerns. The methods and strategies for their implementation in practice are presented. Simulation studies and an application to data from an HIV clinical trial demonstrate the performance of the techniques relative to the existing methods.

Full Text

Duke Authors

Cited Authors

  • Tsiatis, AA; Davidian, M; Zhang, M; Lu, X

Published Date

  • October 15, 2008

Published In

Volume / Issue

  • 27 / 23

Start / End Page

  • 4658 - 4677

PubMed ID

  • 17960577

Pubmed Central ID

  • PMC2562926

International Standard Serial Number (ISSN)

  • 0277-6715

Digital Object Identifier (DOI)

  • 10.1002/sim.3113


  • eng

Conference Location

  • England