A Functional Method for Longitudinal Data with Missing Responses and Covariate Measurement Error

• Activity Number: 379
• Type: Invited
• Date/Time: Tuesday, August 2, 2011 : 2:00 PM to 3:50 PM
• Sponsor: Section on Health Policy Statistics
• Author(s): Grace Y. Yi*+ and Yanyuan Ma and Raymond James Carroll
• Companies: University of Waterloo and Texas A & M University and Texas A & M University
• Keywords: Functional measurement error modeling ; Generalized method of moments ; Inverse probability weighting ; Longitudinal data ; Missing response

Abstract

Covariate measurement error and missing responses are two typical features in longitudinal data analysis. There has been extensive research on either covariate measurement error or missing responses, but relatively little work has been done to address both characteristics simultaneously. In this talk, we propose a simple method for the marginal analysis of longitudinal data with time-varying covariates, some of which are measured with error, while the response is subject to missingness. The proposed method has a number of appealing properties: assumptions on the model are minimal, including no assumptions about the distribution of the mismeasured covariate; implementation is quite straightforward; and the applicability of the proposed method is broad. We provide both theoretical justification and numerical results of our method.