贝叶斯缺陷马尔可夫-奥林戈姆佩茨模型：一种识别治愈分数的综合方法
Bayesian defective Marshall-Olkin Gompertz model: an integrated approach to identifying cure fraction
AI总结：
本文提出一种贝叶斯缺陷回归模型，用于长期生存结果，利用马尔可夫-奥林戈姆佩茨分布，通过蒙特卡洛研究评估其渐近性质，并应用睾丸癌患者数据展示治愈分数的不确定性估计。
摘要：
回归模型在生存分析中治疗解释、遗传特征和其他潜在风险因素的解释具有重大影响。在许多应用中，对删失和生存曲线的描述揭示了数据中的治愈分数，从而导致替代建模。引入协变量的最常用方法是治愈率模型及其变体，尽管缺陷分布的使用引入了更简洁和综合的方法。缺陷分布由密度函数给出，其积分在改变参数域后不是一，使其适合具有明显平台的生存曲线。在本文中，我们引入了一种新的贝叶斯缺陷回归模型，用于长期生存结果，使用马尔可夫-奥林戈姆佩茨分布。估计过程是在贝叶斯范式下进行的。我们评估了在模糊先验方案下的渐近性质，在蒙特卡洛研究中。我们使用来自巴西圣保罗诊断为睾丸癌的患者数据，展示了长期幸存者的识别。提供了具有不确定性估计的治愈情景，通过可信区间来评估风险年龄、治疗存在性和癌症阶段等特征。
英文摘要：
Regression models have a substantial impact on interpretation of treatments, genetic characteristics and other potential risk factors in survival analysis. In many applications, the description of censoring and survival curve reveals the presence of cure fraction on data, which leads to alternative modeling. The most common approach to introduce covariates under a parameter estimation is the cure rate model and its variations, although the use of defective distributions have introduced a more parsimonious and integrated approach. Defective distributions are given by a density function whose integration is not one after changing the domain of one of the parameters, making them appropriate for survival curves with an evident plateau. In this work, we introduce a new Bayesian defective regression model for long-term survival outcomes using the Marshall-Olkin Gompertz distribution. The estimation process is under the Bayesian paradigm. We evaluate the asymptotic properties of our proposal under the vague prior scheme in Monte Carlo studies. We present a motivating real-world application using data from patients diagnosed with testicular cancer in São Paulo, Brazil, in which long-term survivors were identified. Scenarios of cure with uncertainty estimates via credible intervals are provided to evaluate characteristics such as risk age, presence of treatment, and cancer stage.
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