Likelihood Inference for Copula Models Based on Left-Truncated and Competing Risks Data from Field Studies

Hirofumi Michimae and Takeshi Emura
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Hirofumi Michimae: Department of Clinical Medicine (Biostatistics), School of Pharmacy, Kitasato University, Tokyo 108-8641, Japan
Takeshi Emura: Biostatistics Center, Kurume University, Kurume 830-0011, Japan

Mathematics, 2022, vol. 10, issue 13, 1-15

Abstract: Survival and reliability analyses deal with incomplete failure time data, such as censored and truncated data. Recently, the classical left-truncation scheme was generalized to analyze “field data”, defined as samples collected within a fixed period. However, existing competing risks models dealing with left-truncated field data are not flexible enough. We propose copula-based competing risks models for latent failure times, permitting a flexible parametric form. We formulate maximum likelihood estimation methods under the Weibull, lognormal, and gamma distributions for the latent failure times. We conduct simulations to check the performance of the proposed methods. We finally give a real data example. We provide the R code to reproduce the simulations and data analysis results.

Keywords: censoring; competing risk; left-truncation; lognormal distribution; multivariate survival analysis; Weibull distribution; reliability (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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