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<p>Survival analysis encounters problems during the processing of individual distinctions and partial event-time incompleteness because these conditions yield flawed results and inaccurate predictions. The study applies Bayesian optimization methodology to boost the Gompertz Cox model by incorporating Lomax-generalized, odd Lomax-generalized, and inverse Lomax-generalized families of distribution. Statistical distribution types allow survival models to identify patterns that standard models fail to detect. Random effects within this model help it discover unknown individual differences to create predictions that are both accurate and dependable. Through Bayesian statistics, we obtain the model parameter posterior distributions while applying fast Markov Chain Monte Carlo algorithms for their estimation process. The performance assessment consists of a rigorous evaluation through the combination of leave-one-out information criterion with the widely applicable information criterion. Analysis using the Lomax–Gompertz Cox model produces better results when dealing with interval-censored survival data and hidden variations in artificial and genuine datasets when compared to traditional methods. Bayesian optimization fulfills the requirements of survival analysis by providing fast, interpretable calculations that biostatisticians, engineering practitioners, and actuarial scientists can utilize.</p>

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Enhancing Predictive Accuracy of the Gompertz–Cox Model: A Bayesian Survival Analysis with Interval-Censored Data

  • 期刊论文

  • 发表日期：

    2025年10月3日

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  • Mohammad Parvej,
  • Devashish,
  • Fahad Ashraf,
  • Athar Ali Khan

摘要

Survival analysis encounters problems during the processing of individual distinctions and partial event-time incompleteness because these conditions yield flawed results and inaccurate predictions. The study applies Bayesian optimization methodology to boost the Gompertz Cox model by incorporating Lomax-generalized, odd Lomax-generalized, and inverse Lomax-generalized families of distribution. Statistical distribution types allow survival models to identify patterns that standard models fail to detect. Random effects within this model help it discover unknown individual differences to create predictions that are both accurate and dependable. Through Bayesian statistics, we obtain the model parameter posterior distributions while applying fast Markov Chain Monte Carlo algorithms for their estimation process. The performance assessment consists of a rigorous evaluation through the combination of leave-one-out information criterion with the widely applicable information criterion. Analysis using the Lomax–Gompertz Cox model produces better results when dealing with interval-censored survival data and hidden variations in artificial and genuine datasets when compared to traditional methods. Bayesian optimization fulfills the requirements of survival analysis by providing fast, interpretable calculations that biostatisticians, engineering practitioners, and actuarial scientists can utilize.