Beyond the proportional frailty model: Bayesian estimation of individual heterogeneity on mortality parameters. Issue 1 (1st October 2019)

• Record Type: Journal Article
• Authors: Colchero, Fernando; Kiyakoglu, Burhan Y.
• Is Part Of: Biometrical journal. Volume 62:Issue 1(2020:Jan.)
• Page Start: 124
• Page End: 135
• Publication Date: 2019-10-01
• Subjects: Bayesian inference -- frailty -- individual heterogeneity -- mortality -- survival analysis; Biometry -- Periodicals; Medical statistics -- Periodicals; 570.15195
• DOI: 10.1002/bimj.201800280
• Languages: English
• ISSNs: 0323-3847

Abstract

Abstract: Today, we know that demographic rates can be greatly influenced by differences among individuals in their capacity to survive and reproduce. These intrinsic differences, commonly known as individual heterogeneity, can rarely be measured and are thus treated as latent variables when modeling mortality. Finite mixture models and mixed effects models have been proposed as alternative approaches for inference on individual heterogeneity in mortality. However, in general models assume that individual heterogeneity influences mortality proportionally, which limits the possibility to test hypotheses on the effect of individual heterogeneity on other aspects of mortality such as ageing rates. Here, we propose a Bayesian model that builds upon the mixture models previously developed, but that facilitates making inferences on the effect of individual heterogeneity on mortality parameters other than the baseline mortality. As an illustration, we apply this framework to the Gompertz–Makeham mortality model, commonly used in human and wildlife studies, by assuming that the Gompertz rate parameter is affected by individual heterogeneity. We provide results of a simulation study where we show that the model appropriately retrieves the parameters used for simulation, even for low variances in the heterogeneous parameter. We then apply the model to a dataset on captive chimpanzees and on a cohort life table of 1751 Swedish men, and show how model selection against a null model (i.e., without heterogeneity) can be carried out.