title: Analysis of Ordinal Outcomes with Longitudinal Covariates Subject to Missingness.
abstract: We propose a mixture model for data with an ordinal outcome and a longitudinal covariate that is subject to missingness. Data from a tailored telephone delivered, smoking cessation intervention for construction laborers are used to illustrate the method, which considers as an outcome a categorical measure of smoking cessation, and evaluates the effectiveness of the motivational telephone interviews on this outcome. We propose two model structures for the longitudinal covariate, for the case when the missing data are missing at random, and when the missing data mechanism is non-ignorable. A generalized EM algorithm is used to obtain maximum likelihood estimates.
authors.affiliation: Division of Public Health Sciences, Department of Surgery, Washington University in St. Louis School of Medicine, St. Louis, MO, U.S.A.
authors.forename: Melody S
authors.initials: MS
authors.lastname: Goodman
authors.affiliation: Department of Biostatistics, University of Michigan School of Public Health, Anne Arbor, MI, U.S.A.
authors.forename: Yi
authors.initials: Y
authors.lastname: Li
authors.affiliation: Center for Statistical Analysis & Research, New England Research Institute, Watertown, MA, U.S.A.
authors.forename: Anne M
authors.initials: AM
authors.lastname: Stoddard
authors.affiliation: Center for Community Based Research, Dana Farber Cancer Institute, Department of Society, Human Development, and Health, Harvard School of Public Health, Boston, MA, U.S.A.
authors.forename: Glorian
authors.initials: G
authors.lastname: Sorensen
keywords: longitudinal covariates; missingness; ordinal outcomes