Genetic analysis of cause of death in a mixture model of bivariate lifetime data

Abstract

A mixture model in multivariate survival analysis is presented, whereby heterogeneity among subjects creates divergent paths for the individual’s risk of experiencing an event (i.e., disease), as well as for the associated length of survival. Dependence among competing risks is included and rendered testable. This method is an extension of the bivariate correlated gamma-frailty model. It is applied to a data set on Danish twins, for whom cause-specific mortality is known. The use of multivariate data solves the identifiability problem which is inherent in the competing risk model of univariate lifetimes. We analyse the influence of genetic and environmental factors on frailty. Using a sample of 1470 monozygotic (MZ) and 2730 dizygotic (DZ) female twin pairs, we apply five genetic models to the associated mortality data, focusing particularly on death from coronary heart disease (CHD). Using the best fitting model, the inheritance risk of death from CHD was 0.39 (standard error 0.13). The results from this model are compared with the results from earlier analysis that used the restricted model, where the independence of competing risks was assumed. Comparing both cases, it turns out, that heritability of frailty on mortality due to CHD change substantially. Despite the inclusion of dependence, analysis confirms the significant genetic component to an individual’s risk of mortality from CHD. Whether dependence or independence is assumed, the best model for analysis with regard to CHD mortality risks is a model assuming that additive factors are responsible for heritability in susceptibility to CHD. The paper ends with a discussion of limitations and possible further extensions to the model presented.

Keywords

coronary heart disease dependent competing risks frailty mixture models survival analysis

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