Estimating life expectancy of demented and institutionalized subjects from interval-censored observations of a multi-state model

• Published in: Statistical modelling Vol. 9; no. 4; pp. 345 - 360
• Main Authors: Joly, Pierre, Durand, Cécile, Helmer, Catherine, Commenges, Daniel
• Format: Journal Article
• Language: English
• Published: New Delhi, India SAGE Publications 01.12.2009; Sage Publications Ltd
• Subjects: Age; Biochemistry, Molecular Biology; Cohort analysis; Dementia; Epidemiology; Institutionalization; Interval arithmetic; Life expectancy; Life Sciences; Mathematical analysis; Older people; Random variables; Studies; Survival analysis; institutionalization; life expectancy; penalized likelihood; dementia; multi-state model; interval censoring; Life expectancy; Multi-state model; Penalized likelihood; Institutionalization; Interval-censoring; Dementia
• ISSN: 1471-082X; 1477-0342
• DOI: 10.1177/1471082X0900900405

We consider the problem of estimating life expectancy of demented and institutionalized subjects from interval-censored observations. A mixed discrete-continuous scheme of observation is a classical pattern in epidemiology because very often clinical status is assessed at discrete visit times while times of death or other events can be exactly observed. In this work, we jointly modelled dementia, institutionalization and death from data of a cohort study. Due to discrete time observations, it may happen that a subject developed dementia or was institutionalized between the last visit and the death. Consequently, there is an uncertainty about the precise number of diseased or institutionalized subjects. Moreover, the time of onset of dementia is interval censored. We use a penalized likelihood approach for estimating the transition intensities of the multi-state model. With these estimators, incidence and life expectancy can be computed easily. This approach deals with incomplete data due to the presence of left truncation and interval censoring. It can be generalized to take explanatory variables into account. We illustrate this approach by applying this model to the analysis of a large cohort study on cerebral ageing.