Improving efficiency of inferences in randomized clinical
trials using auxiliary covariates
Min Zhang
PhD Candidatete in Statistics
North Carolina State University
Wednesday, February 20, 2008
12:15 pm
5275 MSC
| ABSTRACT |
The primary goal of a randomized clinical trial is to make comparisons among two or more treatments. In general, comparisons may be based on meaningful parameters in a relevant statistical model; for example, pairwise odds-ratios or log-odds ratios may be used when the outcome is binary. Standard analyses for estimation and testing in this context typically are based on the data collected on response and treatment assignment only. In many trials, auxiliary baseline covariate information may also be available, and it is of interest to exploit these data to improve the efficiency of inferences. In this talk, we take a semiparametric theory perspective and present a broadly-applicable approach to adjustment for auxiliary covariates to achieve more efficient estimators and tests for treatment parameters in the analysis of randomized clinical trials. Unlike the usual adjustment via a regression model for mean outcome as a function of treatment assignment and covariates, this approach separates estimation of treatment effect from modeling the relationships of outcome to covariates, which may lessen concerns over bias and subjectivity associated with regression-based adjustment.
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