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Gamma shared frailty model based on reversed hazard rate

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  • David D. Hanagal
  • Arvind Pandey

Abstract

Frailty models are used in survival analysis to account for unobserved heterogeneity in individual risks to disease and death. To analyze bivariate data on related survival times (e.g., matched pairs experiments, twin, or family data), shared frailty models were suggested. Shared frailty models are frequently used to model heterogeneity in survival analysis. The most common shared frailty model is a model in which hazard function is a product of random factor(frailty) and baseline hazard function which is common to all individuals. There are certain assumptions about the baseline distribution and distribution of frailty. In this paper, we introduce shared gamma frailty models with reversed hazard rate. We introduce Bayesian estimation procedure using Markov Chain Monte Carlo (MCMC) technique to estimate the parameters involved in the model. We present a simulation study to compare the true values of the parameters with the estimated values. Also, we apply the proposed model to the Australian twin data set.

Suggested Citation

  • David D. Hanagal & Arvind Pandey, 2016. "Gamma shared frailty model based on reversed hazard rate," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(7), pages 2071-2088, April.
    • Handle: RePEc:taf:lstaxx:v:45:y:2016:i:7:p:2071-2088
    DOI: 10.1080/03610926.2013.870204
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    Cited by:

    1. Man-Ho Ling & Narayanaswamy Balakrishnan & Chenxi Yu & Hon Yiu So, 2021. "Inference for One-Shot Devices with Dependent k -Out-of- M Structured Components under Gamma Frailty," Mathematics, MDPI, vol. 9(23), pages 1-24, November.

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