ABSTRACT Joint models for longitudinal and survival data are routinely used in clinical trials or other studies to assess a treatment effect while accounting for longitudinal measures such as patient-reported outcomes. In the Bayesian framework, the deviance information criterion (DIC) and the logarithm of the pseudo-marginal likelihood (LPML) are two well-known Bayesian criteria for comparing joint models. However, these criteria do not provide separate assessments of each component of the joint model. In this article, we develop a novel decomposition of DIC and LPML to assess the fit of the longitudinal and survival components of the joint model, separately. Based on this decomposition, we then propose new Bayesian model assessment criteria, namely, ΔDIC and ΔLPML, to determine the importance and contribution of the longitudinal (survival) data to the model fit of the survival (longitudinal) data. Moreover, we develop an efficient Monte Carlo method for computing the conditional predictive ordinate statistics in the joint modeling setting. A simulation study is conducted to examine the empirical performance of the proposed criteria and the proposed methodology is further applied to a case study in mesothelioma. Supplementary materials for this article are available online.

中文翻译：

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用于癌症临床试验的纵向和生存数据联合建模中的贝叶斯模型评估

摘要 纵向和生存数据的联合模型通常用于临床试验或其他研究，以评估治疗效果，同时考虑纵向测量，例如患者报告的结果。在贝叶斯框架中，偏差信息准则（DIC）和伪边际似然对数（LPML）是比较联合模型的两个众所周知的贝叶斯准则。但是，这些标准并未对联合模型的每个组成部分提供单独的评估。在本文中，我们开发了 DIC 和 LPML 的新分解，以分别评估联合模型的纵向和生存分量的拟合。基于这种分解，我们提出了新的贝叶斯模型评估标准，即ΔDIC和ΔLPML，确定纵向（生存）数据对生存（纵向）数据模型拟合的重要性和贡献。此外，我们开发了一种有效的蒙特卡罗方法，用于计算联合建模设置中的条件预测纵坐标统计量。进行模拟研究以检查所提出标准的经验表现，并将所提出的方法进一步应用于间皮瘤的案例研究。本文的补充材料可在线获取。进行模拟研究以检查所提出标准的经验表现，并将所提出的方法进一步应用于间皮瘤的案例研究。本文的补充材料可在线获取。进行模拟研究以检查所提出标准的经验表现，并将所提出的方法进一步应用于间皮瘤的案例研究。本文的补充材料可在线获取。