title: Logistic regression models with missing covariate values for complex survey data.
abstract: Maximum likelihood methods are used to incorporate partially observed covariate values in fitting logistic regression models. We extend these methods to data collected through complex surveys using the pseudo-likelihood approach. One can obtain parameter estimates of the logistic regression model using standard statistical software and their standard errors by Taylor series expansion or the jackknife method. We apply the approach to data from a two-phase survey screening for dementia in a community sample of African Americans age 65 and older living in Indianapolis. The binary response variable is dementia and the covariate with missing values is a daily functioning score collected from interviews with a relative of the study subject.
authors.affiliation: Department of Medicine, Indiana University School of Medicine, Indianapolis 46202-5200, USA.
authors.forename: S
authors.initials: S
authors.lastname: Gao
authors.forename: S L
authors.initials: SL
authors.lastname: Hui
mesh_terms: D001741:Black or African American; D000368:Aged; D003071:Cognition; D003704:Dementia / Q000175:diagnosis / Q000453:epidemiology; D005260:Female; D006306:Health Surveys; D006801:Humans; D007196:Indiana / Q000453:epidemiology; D007407:Interviews as Topic; D016013:Likelihood Functions; D016015:Logistic Models; D008297:Male; D015995:Prevalence; D012984:Software