Estimating hidden morbidity via its effect on mortality and disability.

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Woodbury, MA; Manton, KG; Yashin, AI

Published Date

  • January 1988

Published In

Volume / Issue

  • 7 / 1-2

Start / End Page

  • 325 - 336

PubMed ID

  • 3353611

International Standard Serial Number (ISSN)

  • 0277-6715

Language

  • eng

Conference Location

  • England