Weighted estimating equations for longitudinal studies with death and non-monotone missing time-dependent covariates and outcomes.
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.
Shardell
Michelle
M
Department of Epidemiology and Preventive Medicine, University of Maryland, Baltimore, MD 21201, U.S.A. mshardel@epi.umaryland.edu
Miller
Ram R
RR
D000368:Aged; D015142:Baltimore / Q000453:epidemiology; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D003643:Death; D005260:Female; D005853:Geriatrics / Q000706:statistics & numerical data; D006620:Hip Fractures / Q000534:rehabilitation; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D012044:Regression Analysis