The North Carolina Chapter of The American Statistical Association NC-ASA Lecture Series
The North Carolina Chapter of the American Statistical Association is
pleased to announce the following lecture(s)Title: Improving efficiency of inferences in randomized clinical trials using
auxiliary covariates"Speaker: Dr. Marie Davidian,
William Neal Reynolds Distinguished Professor of Statistics
Director, Center for Quantitative Sciences in Biomedicine,
North Carolina State University
Time: 5:30PM, Thursday, May 22, 2008
Place: Lecture Hall, National Institute of Statistical Sciences (NISS),
Research Triangle Park, NC (map and Directions: http://www.niss.org/directions.html ).Please join us Thursday May 22, 2008, at 5:30 pm for refreshments at the
Lecture Hall, National Institute of Statistical Sciences (NISS) in
Research Triangle Park, NCAbstract: The primary goal of a randomized clinical trial is to make comparisons
among two or more treatments. For example, in a two-arm trial with
continuous response, the focus may be on the difference in treatment
means; with more than two treatments, the comparison may be based on
pairwise differences. With binary outcome, pairwise odds-ratios or
log-odds ratios may be used. In general, comparisons may be based on
meaningful parameters in a relevant statistical model. 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 there has been considerable debate regarding
whether and how these data should be used to improve the efficiency of
inferences. Taking a semiparametric theory perspective, we propose a
broadly-applicable approach to achieving more efficient estimators and
tests in the analysis of randomized clinical trials, where
``adjustment'' for auxiliary covariates is carried out in such a way
that concerns over the potential for bias and subjectivity often
raised for other covariate adjustment methods may be
obviated. Simulations and applications demonstrate the performance of
the methods.
*This is joint work with Min Zhang, Xiaomin Lu, and Anastasios Tsiatis
