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.