Projecting disease incidence, prevalence, and net morbidity is often needed when individuals are likely to die, either disease free or after the disease has developed.
Examples of this include remission of cancer or heart disease in elderly people who can die from these or other causes and occurrence of a particular acquired immune deficiency syndrome illness in human immunodeficiency virus type 1 (HIV-1) disease.
Death is not an ancillary event but, rather, indicates either an end to disease morbidity or an end to risk to ever develop that disease.
Thus, time to disease survival analyses that censor disease-free individuals at death can produce misleading results.
This paper describes several useful quantifications of disease and death for this setting.
A paradigm that utilizes Kaplan-Meier functions to estimate these quantities is introduced.
The approach anchors on a four-stage disease/death model :
stage A, living without disease ;
stage B, dead without ever developing disease ;
stage C, developed the disease and living ;
and stage D, dead after developing the disease.
An application is made to projecting cytomegalovirus disease in a cohort of HIV-1-infected users of zidovudine and Pneumocystis prophylaxis from the Multicenter AIDS Cohort Study (MACS) during 1989-1993.
At 3 years after a CD4+count below 100/mul, a man had an 18.7%, 46.3%, 5.3%, or 29.9% chance, respectively, to be in stage A, B, C, or D. This man, on average, had 0.28 years of cytomega...
Mots-clés Pascal : Modèle prévision, Maladie, Morbidité, Prévalence, Incidence, Modèle statistique, Homme, Epidémiologie, Mort, Estimateur Kaplan Meier
Mots-clés Pascal anglais : Forecast model, Disease, Morbidity, Prevalence, Incidence, Statistical model, Human, Epidemiology, Death, Kaplan Meier estimator
Notice produite par :
Inist-CNRS - Institut de l'Information Scientifique et Technique
Cote : 96-0254314
Code Inist : 002B30A01A1. Création : 199608.