Bayesian parametric estimation based on left-truncated competing risks data under bivariate Clayton copula models

Hirofumi Michimae, Takeshi Emura, Atsushi Miyamoto and Kazuma Kishi

Journal of Applied Statistics, 2024, vol. 51, issue 13, 2690-2708

Abstract: In observational/field studies, competing risks and left-truncation may co-exist, yielding ‘left-truncated competing risks’ settings. Under the assumption of independent competing risks, parametric estimation methods were developed for left-truncated competing risks data. However, competing risks may be dependent in real applications. In this paper, we propose a Bayesian estimator for both independent competing risks and copula-based dependent competing risks models under left-truncation. The simulations show that the Bayesian estimator for the copula-based dependent risks model yields the desired performance when competing risks are dependent. We also comprehensively explore the choice of the prior distributions (Gamma, Inverse-Gamma, Uniform, half Normal and half Cauchy) and hyperparameters via simulations. Finally, two real datasets are analyzed to demonstrate the proposed estimators.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:51:y:2024:i:13:p:2690-2708

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DOI: 10.1080/02664763.2024.2315458

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