Alvares Danilo, Meza Cristian, De la Cruz Rolando
MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
INGEMAT-CIMFAV, Faculty of Engineering, Universidad de Valparaíso, Valparaiso, Chile.
Stat Methods Med Res. 2025 Aug;34(8):1525-1533. doi: 10.1177/09622802251345485. Epub 2025 May 29.
Motivated by a pregnancy miscarriage study, we propose a Bayesian joint model for longitudinal and time-to-event outcomes that takes into account different complexities of the problem. In particular, the longitudinal process is modeled by means of a nonlinear specification with subject-specific error variance. In addition, the exact time of fetal death is unknown, and a subgroup of women is not susceptible to miscarriage. Hence, we model the survival process via a mixture cure model for interval-censored data. Finally, both processes are linked through the subject-specific longitudinal mean and variance. A simulation study is conducted in order to validate our joint model. In the real application, we use individual weighted and Cox-Snell residuals to assess the goodness-of-fit of our proposal versus a joint model that shares only the subject-specific longitudinal mean (standard approach). In addition, the leave-one-out cross-validation criterion is applied to compare the predictive ability of both models.
受一项妊娠流产研究的启发，我们提出了一种用于纵向和事件发生时间结局的贝叶斯联合模型，该模型考虑了问题的不同复杂性。具体而言，纵向过程通过具有个体特定误差方差的非线性规范进行建模。此外，胎儿死亡的确切时间未知，并且有一部分女性不易流产。因此，我们通过用于区间删失数据的混合治愈模型对生存过程进行建模。最后，两个过程通过个体特定的纵向均值和方差联系起来。进行了一项模拟研究以验证我们的联合模型。在实际应用中，我们使用个体加权和Cox-Snell残差来评估我们的模型与仅共享个体特定纵向均值的联合模型（标准方法）相比的拟合优度。此外，应用留一法交叉验证准则来比较两个模型的预测能力。
