JSM 2004 - Toronto

Abstract #300247

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Activity Number: 143
Type: Invited
Date/Time: Monday, August 9, 2004 : 2:00 PM to 3:50 PM
Sponsor: International Chinese Statistical Association
Abstract - #300247
Title: New Statistical Methods for the Analysis of High-dimensional Longitudinal Data
Author(s): James Robins*+ and Andrea Rotnitzky and Aad van der Vaart
Companies: Harvard School of Public Health and Harvard School of Public Health and Vrije University
Address: 677 Huntington Avenue, Boston, MA, 02115,
Keywords: longitudinal data ; causality ; Bayes-Frequentist ; semiparametric
Abstract:

We discuss difficult or nonstandard problems that arise in estimation of optimal treatment strategies from high-dimensional longitudinal data, including interval estimation for nonregular parameters. We describe a new form of Bayes -frequentist compromise that allows us to incorporate doubly robust (DR) estimating functions (with their desirable frequentist performance) into a formal Bayesian decision analysis in which we explicitly allow for (1) confounding by unmeasured factors, (2) misspecification of models for the conditional expectation of the counterfactual response under the "optimal regime" and models for treatment assignment that are necessary for construction of DR estimators, and (3) misspecification of the optimal-regime structural nested model itself.

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