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Covariate adjustment for twosample treatment comparisons in randomized clinical trials: a principled yet flexible approach (2007).
| Content Provider | CiteSeerX |
|---|---|
| Author | Tsiatis, Anastasios A. Davidian, Marie Zhang, Min Lu, Xiaomin |
| Abstract | 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 existing methods. Copyright c © 2000 John Wiley & Sons, Ltd. key words: baseline variables; clinical trials; covariate adjustment; efficiency; semiparametric theory; variable selection 1. |
| File Format | |
| Publisher Date | 2007-01-01 |
| Access Restriction | Open |
| Subject Keyword | Covariate Adjustment Randomized Clinical Trial Twosample Treatment Comparison Flexible Approach Desired Gain Post Hoc Hiv Clinical Trial Demonstrate Allow Covariate Relationship Fishing Expedition John Wiley Son Covariate Adjusted Analysis Treatment Effect Clinical Trial Treatment Effect Estimator Baseline Variable Semiparametric Theory Substantial Baseline Covariate Information Dramatic Effect Estimate Simulation Study Practically-feasible Method Variable Selection Usual Concern Considerable Debate |
| Content Type | Text |
