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

Author

Listed:
  • 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
    Research Center for Medical and Health Data Science, The Institute of Statistical Mathematics, Tokyo 190-8562, Japan)

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.

Suggested Citation

  • Hirofumi Michimae & Takeshi Emura, 2022. "Likelihood Inference for Copula Models Based on Left-Truncated and Competing Risks Data from Field Studies," Mathematics, MDPI, vol. 10(13), pages 1-15, June.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:13:p:2163-:d:844028
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    References listed on IDEAS

    as
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    1. Jan Górecki, 2024. "Pair programming with ChatGPT for sampling and estimation of copulas," Computational Statistics, Springer, vol. 39(6), pages 3231-3261, September.

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