Abstract
A major challenge for inference regarding aging-related change in longitudinal studies is that of study attrition and population mortality. Inferences in longitudinal studies can account for attrition and mortality-related change as distinct processes, but this is made difficult when follow-up of all individuals (i.e., age at death) is not complete. This is a common problem because most longitudinal studies of aging either have incomplete follow-up or are still collecting data on subsequent outcomes, including time of death. A statistical approach is suggested for including time-to-death as a predictor in models with incomplete follow-up using a two-stage multiple-imputation procedure. An empirical example using data from the OCTO-Twin study is presented that shows the utility of his procedure for making inferences conditional on mortality when mortality data are incomplete.
Original language | English (US) |
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Pages (from-to) | 187-203 |
Number of pages | 17 |
Journal | Experimental Aging Research |
Volume | 33 |
Issue number | 2 |
DOIs | |
State | Published - Apr 2007 |
ASJC Scopus subject areas
- Aging
- Arts and Humanities (miscellaneous)
- General Psychology
- Geriatrics and Gerontology