JSM 2004 - Toronto

Abstract #301092

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Activity Number: 273
Type: Contributed
Date/Time: Tuesday, August 10, 2004 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #301092
Title: Mixed Effects Models for Truncated Longitudinal Outcome and Nonignorable Missing Data
Author(s): Sujuan Gao*+
Companies: Indiana University School of Medicine
Address: Division of Biostatistics, Indianapolis, IN, 46202,
Keywords: mixed effect models ; truncation ; non-ignorable missing ; EM algorithm
Abstract:

Mixed effects models are often used for estimating fixed effects and variance components in continuous longitudinal outcomes. Hughes proposed an EM based estimation approach for mixed effects models when the outcomes are truncated. We consider the situation when the longitudinal outcomes are also subject to nonignorable missing in addition to truncation. A selection model approach is proposed to model the joint likelihood of observed data by modeling the probability of missing data to depend on the unobserved outcomes. Sensitivity analyses will be conducted to examine the effect of the missing data assumption on the parameter estimates. Data from neuropsychological testing of dementia will be used to illustrate the proposed approach.

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