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A Semiparametric Transition Model with Latent Traits for Longitudinal Multistate Data
Author(s) -
Lin Haiqun,
Guo Zhenchao,
Peduzzi Peter N.,
Gill Thomas M.,
Allore Heather G.
Publication year - 2008
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2008.01011.x
Subject(s) - covariate , trait , multivariate statistics , nonparametric statistics , local independence , factor analysis , statistics , correlation , econometrics , independence (probability theory) , semiparametric regression , semiparametric model , latent variable , latent variable model , mathematics , computer science , geometry , programming language
Summary We propose a general multistate transition model. The model is developed for the analysis of repeated episodes of multiple states representing different health status. Transitions among multiple states are modeled jointly using multivariate latent traits with factor loadings. Different types of state transition are described by flexible transition‐specific nonparametric baseline intensities. A state‐specific latent trait is used to capture individual tendency of the sojourn in the state that cannot be explained by covariates and to account for correlation among repeated sojourns in the same state within an individual. Correlation among sojourns across different states within an individual is accounted for by the correlation between the different latent traits. The factor loadings for a latent trait accommodate the dependence of the transitions to different competing states from a same state. We obtain the semiparametric maximum likelihood estimates through an expectation‐maximization (EM) algorithm. The method is illustrated by studying repeated transitions between independence and disability states of activities of daily living (ADL) with death as an absorbing state in a longitudinal aging study. The performance of the estimation procedure is assessed by simulation studies.