Estimating hidden morbidity via its effect on mortality and disability

Woodbury MA, Manton KG, & Yashin AI (1988). Estimating hidden morbidity via its effect on mortality and disability. Statistics in Medicine 7 (1-2): 325-336. DOI:10.1002/sim.4780070133.

Full text not available from this repository.

Abstract

The applicability of the theory of partially observed finite-state Markov processes to the study of disease. morbidity, and disability is explored. A method is developed for the continuous updating of parameter estimates over time in longitudinal studies analogous to Kalman filtering in continuous valued continuous time stochastic processes. It builds on a model of filtering of incompletely observed finite-state Markov processes subject to mortality due to Yashin et al. The method of estimation is based on maximum likelihood theory and the incompleteness in the observation of the process is dealt with by applying missing information principles in maximum likelihood estimation.

Item Type: Article
Research Programs: World Population (POP)
Depositing User: Romeo Molina
Date Deposited: 24 Aug 2016 09:54
Last Modified: 24 Aug 2016 09:54
URI: http://pure.iiasa.ac.at/13758

Actions (login required)

View Item View Item

International Institute for Applied Systems Analysis (IIASA)
Schlossplatz 1, A-2361 Laxenburg, Austria
Phone: (+43 2236) 807 0 Fax:(+43 2236) 71 313