RT Journal Article
SR 00
ID 10.1007/BF00275995
A1 Yashin, A.
A1 Manton, K.G.
A1 Stallard, E.
T1 Dependent competing risks: a stochastic process model
JF Journal of Mathematical Biology
YR 1986
FD 1986-07
VO 24
IS 2
SP 119
OP 140
K1 Chronic disease; Cohort study; Diffusion; Framingham heart study; Human mortality; Maximum likelihood; Mortality selection; Survival with covariates
AB Analyses of human mortality data classified according to cause of death frequently are based on competing risk theory. In particular, the times to death for different causes often are assumed to be independent. In this paper, a competing risk model with a weaker assumption of conditional independence of the times to death, given an assumed stochastic covariate process, is developed and applied to cause specific mortality data from the Framingham Heart Study. The results generated under this conditional independence model are compared with analogous results under the standard marginal independence model. Under the assumption that this conditional independence model is valid, the comparison suggests that the standard model overestimates by 4% the effect on life expectancy at age 30 due to the hypothetical elimination of cancer and by 7% the effect for cardiovascular/cerebrovascular disease. By age 80 the overestimates were 11% for cancer and 16% for heart disease. These results suggest the importance of avoiding the marginal independence assumption when appropriate data are available - especially when focusing on mortality at advanced ages.
SN 0303-6812
LK https://pure.iiasa.ac.at/id/eprint/13648/