title: Semiparametric regression models for repeated measures of mortal cohorts with non-monotone missing outcomes and time-dependent covariates.
abstract: We propose a semiparametric marginal modeling approach for longitudinal analysis of cohorts with data missing due to death and non-response to estimate regression parameters interpreted as conditioned on being alive. Our proposed method accommodates outcomes and time-dependent covariates that are missing not at random with non-monotone missingness patterns via inverse-probability weighting. Missing covariates are replaced by consistent estimates derived from a simultaneously solved inverse-probability-weighted estimating equation. Thus, we utilize data points with the observed outcomes and missing covariates beyond the estimated weights while avoiding numerical methods to integrate over missing covariates. The approach is applied to a cohort of elderly female hip fracture patients to estimate the prevalence of walking disability over time as a function of body composition, inflammation, and age.
authors.affiliation: Department of Epidemiology and Preventive Medicine, University of Maryland, Baltimore, MD, USA. mshardel@epi.umaryland.edu
authors.forename: Michelle
authors.initials: M
authors.lastname: Shardell
authors.forename: Gregory E
authors.initials: GE
authors.lastname: Hicks
authors.forename: Ram R
authors.initials: RR
authors.lastname: Miller
authors.forename: Jay
authors.initials: J
authors.lastname: Magaziner
mesh_terms: D000203:Activities of Daily Living; D000368:Aged; D015331:Cohort Studies; D003198:Computer Simulation; D005260:Female; D006620:Hip Fractures / Q000503:physiopathology; D006801:Humans; D008137:Longitudinal Studies; D008954:Models, Biological; D015233:Models, Statistical; D016896:Treatment Outcome; D016138:Walking / Q000502:physiology