Modeling and Validation for Tackling Risk Prediction with Competing Risks by Integrating Multiple Longitudinal Biomarkers

摘要/项目摘要 移植等外科治疗往往对风险预测提出了相当大的分析挑战。 为了死亡。例如，儿科急性肝功能衰竭(PALF)呼叫中的疾病预后和治疗决策 寻找一个可靠的工具来预测死亡风险。然而，这一预测工具的发展受到了 高频率的肝移植(LTX)，其发生与患者的病程密切相关(fi 并独立地审查感兴趣的死亡事件。现有的竞争风险方法不太适合 PALF的风险预测。也认识到多个纵向生物标志物的实质性预后价值 作为基线协变量，我们的目标是在存在治疗引起的竞争风险的情况下解决风险预测问题，方法是 开发、实施和应用合理且在计算上可行的建模、验证和推理 程序。在这个项目中，(目标1)团队提出了一个建模框架，该框架解决了以下问题： 通过聚合来自多个纵向和基线协变量的信息，在死亡和LTX之间进行比较。什么时候 与现有的建模方法相比，提出的建模策略可以更多地集成来自纵向的信息 生物标志物，以更好地捕捉患者的动态疾病状态。接下来，(目标2)我们提出了一套全面的 在存在竞争风险的情况下评估预测性能的验证程序。这些方法 评估累积发病率预测和边际概率预测的预测性能 从各个角度确定和提高预测性能。我们还制定了正式的测试程序 检测不同亚型患者之间潜在的预测性异质性。此外，我们提出(目标 3)在因果推理框架下检验LTx-Benefit的统计程序，容纳了对象- Speciific Benefit为个性化的LTX决策提供信息。所有的统计方法都将严格公正地通过 广泛的模拟研究、敏感性分析和理论推导，以确保其理论严谨性和 实用价值。这些方法将系统地应用于最近的PALF登记册数据库。fiNAL 将通过用户友好的网络界面(目标4)向从业人员传播预测工具，以促进 PALF预测和动态预测。我们预计，我们的方法将广泛适用于其他 临床研究，并将为更广泛的研究社区开发R包。 1

ABSTRACT/PROJECT SUMMARY Surgical treatments such as transplantation often pose considerable analytic challenges to risk prediction for mortality. For example, disease prognosis and treatment decisions in pediatric acute liver failure (PALF) calls for a reliable tool to predict mortality risk. However, the development of this prediction tool is hampered by the high frequency of liver transplantation (LTx), the occurrence of which modifies the disease course of the patient and dependently censors the death event of interest. Existing competing risks methods are not well suited to risk prediction for PALF. Recognizing the substantial prognostic value in multiple longitudinal biomarkers as well as baseline covariates, we aim to tackle risk prediction in the presence of treatment-induced competing risks by developing, implementing and applying sensible and computationally feasible modeling, validation and inference procedures. In this project, (Aim 1) the team proposes a modeling framework that tackles the dependence be- tween death and LTx through aggregating information from multiple longitudinal and baseline covariates. When compared to existing methods, the proposed modeling strategy can integrate information from more longitudinal biomarkers to better capture patients' dynamic disease status. Next, (Aim 2) we propose a comprehensive set of validation procedures to evaluate prediction performance in the presence of competing risks. The methods assess prediction performances in both cumulative incidence prediction and marginal probability prediction to ascertain and enhance prediction performance from all angles. We also develop formal testing procedures to detect potential predictive heterogeneity among different subtypes of patients. Moreover, we propose (Aim 3) statistical procedures to examine LTx-benefit under a causal inference framework, accommodating subject- specific benefit to inform personalized LTx decisions. All statistical methods will be rigorously justified through extensive simulation studies, sensitivity analysis and theoretical derivations, to ensure their theoretical rigor and practical usefulness. The methods will be systematically applied to a recent PALF registry database. The final prediction tool will be disseminated to practitioners through a user-friendly web-interface (Aim 4), to facilitate PALF prediction and dynamic prediction. We anticipate that our methods will be broadly applicable to other clinical studies and will develop R packages for the broader research community. 1