Patient mortality after listing for a solid organ transplant is a relevant, patient‐centric metric, but risk factors for patient mortality after listing present severe non‐proportional hazards. We propose piecewise exponential models (PEMs) with time‐varying effects to account for the non‐proportional hazards, and we use the LASSO to minimize the risk of overfitting. We consider two parameterizations of a PEM: The first model has an overall effect in addition to the time‐varying effects (PEM‐TID), whereas the second model has only time‐varying effects (PEM‐TD). Because the LASSO can shrink every time‐varying effect to 0, risk factors in the PEM‐TID model can have proportional effects during follow‐up. In contrast, covariates in the PEM‐TD model must have different or no effects during follow‐up. These characteristics were illustrated for patients listed for liver transplant. The PEM‐TID model had similar or better predictive performance than the PEM‐TD model, and both were better than the Cox proportional hazards model. Thus, PEMs with time‐varying effects can improve predictive performance for patient mortality after listing for a solid organ transplant.

中文翻译：

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具有时变效应的分段指数模型：列出实体器官移植后的死亡率

列出实体器官移植后的患者死亡率是一个以患者为中心的相关指标，但列出后患者死亡的危险因素存在严重的非比例风险。我们提出了具有时变效应的分段指数模型（PEM），以解决非比例风险，并且我们使用LASSO来最大限度地降低过度拟合的风险。我们考虑了PEM的两个参数化：第一个模型除了具有时变效应（PEM-TID）之外，还具有总体效应，而第二个模型仅具有时变效应（PEM-TD）。因为LASSO可以将每次随时间变化的影响缩小到0，所以PEM-TID模型中的风险因素在后续过程中可能具有成比例的影响。相比之下，PEM-TD模型中的协变量在随访期间必须具有不同或没有影响。列出了肝移植患者的这些特征。与PEM-TD模型相比，PEM-TID模型具有相似或更好的预测性能，并且均优于Cox比例风险模型。因此，具有时变效应的PEM可以在列出实体器官移植后提高患者死亡率的预测性能。