Abstract #301227

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JSM 2003 Abstract #301227
Activity Number: 178
Type: Contributed
Date/Time: Monday, August 4, 2003 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #301227
Title: Targets of Inference for Longitudinal Data Censored by Dropout and Death
Author(s): Brenda F. Kurland*+ and Patrick J. Heagerty
Companies: National Alzheimer's Coordinating Center and University of Washington
Address: Univ. of Washington, Dept. of Epidemiology, Seattle, WA, 98105-6099,
Keywords: missing data ; longitudinal data ; competing risks
Abstract:

Should data missing due to death be recovered? If attrition due to death is not accommodated, group means over time may not follow the functional form of individual trajectories. However, if responses are projected beyond the time of death, parameter estimates may not reflect values for the (living) population. We present examples of regression models for longitudinal observational studies, motivated by collaborative studies of the National Alzheimer's Coordinating Center (NACC) and other geriatric studies. In each regression model, responses are conditional (or not conditional) on subjects' being alive at the time of response. We then describe the implementation of these regression models using standard statistical methods such as linear mixed models, GEE, and logistic regression.

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