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Weighted estimating equations for longitudinal studies with death and non‐monotone missing time‐dependent covariates and outcomes
Author(s) -
Shardell Michelle,
Miller Ram R.
Publication year - 2007
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.2964
Subject(s) - covariate , missing data , outcome (game theory) , estimating equations , statistics , monotone polygon , random effects model , longitudinal data , generalized estimating equation , econometrics , mathematics , computer science , medicine , maximum likelihood , data mining , meta analysis , geometry , mathematical economics
Abstract We propose a marginal modeling approach to estimate the association between a time‐dependent covariate and an outcome in longitudinal studies where some study participants die during follow‐up and both variables have non‐monotone response patterns. The proposed method is an extension of weighted estimating equations that allows the outcome and covariate to have different missing‐data patterns. We present methods for both random and non‐random missing‐data mechanisms. A study of functional recovery in a cohort of elderly female hip‐fracture patients motivates the approach. Copyright © 2007 John Wiley & Sons, Ltd.