Dependent competing risks: a stochastic process model

Yashin A, Manton KG, & Stallard E (1986). Dependent competing risks: a stochastic process model. Journal of Mathematical Biology 24 (2): 119-140. DOI:10.1007/BF00275995.

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Abstract

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.

Item Type: Article
Uncontrolled Keywords: Chronic disease; Cohort study; Diffusion; Framingham heart study; Human mortality; Maximum likelihood; Mortality selection; Survival with covariates
Depositing User: Luke Kirwan
Date Deposited: 09 Aug 2016 13:46
Last Modified: 25 Jan 2017 15:32
URI: http://pure.iiasa.ac.at/13648

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