Population inference with mortality and attrition in longitudinal studies on aging: a two-stage multiple imputation method.
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.
Harel
Ofer
O
Department of Statistics, University of Connecticut, Storrs, Connecticut 06269-4120, USA. oharel@stat.uconn.edu
Hofer
Scott M
SM
Hoffman
Lesa
L
Pedersen
Nancy L
NL
Johansson
Boo
B
D000367:Age Factors; D000369:Aged, 80 and over; D000375:Aging / Q000502:physiology*; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D008954:Models, Biological; D009026:Mortality; D012737:Sex Factors; D015996:Survival Rate; D018433:Twin Studies as Topic