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A shared random effect parameter approach for longitudinal dementia data with non‐ignorable missing data
Author(s) -
Gao Sujuan
Publication year - 2004
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.1710
Subject(s) - missing data , laplace's method , likelihood function , statistics , inference , random effects model , computer science , statistical inference , laplace transform , mathematics , econometrics , maximum likelihood , bayesian probability , artificial intelligence , medicine , meta analysis , mathematical analysis
A significant source of missing data in longitudinal epidemiologic studies on elderly individuals is death. It is generally believed that these missing data by death are non‐ignorable to likelihood based inference. Inference based on data only from surviving participants in the study may lead to biased results. In this paper we model both the probability of disease and the probability of death using shared random effect parameters. We also propose to use the Laplace approximation for obtaining an approximate likelihood function so that high dimensional integration over the distributions of the random effect parameters is not necessary. Parameter estimates can be obtained by maximizing the approximate log‐likelihood function. Data from a longitudinal dementia study will be used to illustrate the approach. A small simulation is conducted to compare parameter estimates from the proposed method to the ‘naive’ method where missing data is considered at random. Copyright © 2004 John Wiley & Sons, Ltd.