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

通过整合多个纵向生物标志物来解决具有竞争风险的风险预测的建模和验证

• 批准号: 9922892
• 负责人: Ruosha Li
• 金额: $ 35.04万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-19 至 2022-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9922892
• 关键词: Acute Liver Failure; Biological Markers; Cessation of life; Child; Childhood; Clinical; Clinical Research; Communities; Complex; Computer software; Critical Illness; Data; Databases; Decision Making; Dependence; Derivation procedure; Development; Disease; Ensure; Equine mule; Event; Frequencies; Future; Goals; Heterogeneity; Incidence; Joints; Life; Methods; Modeling; Operative Surgical Procedures; Organ failure; Outcome; Patients; Performance; Prediction of Response to Therapy; Probability; Procedures; Registries; Research; Risk; Risk Factors; Scheme; Selection Bias; Statistical Methods; Syndrome; Techniques; Testing; Time; Transplantation; Tweens; United States; Validation; Work; base; interest; liver function; liver transplantation; mortality; mortality risk; novel; outcome forecast; patient subsets; predictive modeling; prognostic value; public health relevance; simulation; tool; user friendly software; user-friendly; web interface

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

摘要/项目总结 手术治疗，如移植，经常对风险预测提出相当大的分析挑战 死亡率。例如，儿科急性肝功能衰竭（PALF）的疾病预后和治疗决策要求 一个可靠的工具来预测死亡风险。然而，这种预测工具的发展受到了 肝移植（LTx）频率高，其发生改变了患者的病程 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诱导的竞争性风险的情况下，通过以下方法来解决风险预测问题： 开发，实施和应用合理的和计算上可行的建模，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，解决了依赖性， 通过汇总来自多个纵向和基线协变量的信息，分析死亡和LTx之间的关系。当 与现有方法相比，所提出的建模策略可以整合来自更纵向的信息， 生物标志物，以更好地捕捉患者的动态疾病状态。其次，（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。的方法 评估累积发生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。我们还开发正式的测试程序 以检测不同亚型患者之间潜在的预测异质性。此外，我们还提出（Aim 3)在因果推理框架下检查LTx效益的统计程序，适应受试者- 为个性化的LTx决策提供信息。所有的统计方法都将通过以下方式得到严格的艾德： 广泛的模拟研究、灵敏度分析和理论推导，以确保其理论的严谨性， 实用性。这些方法将系统地应用于最近的PALF登记数据库。最后 预测工具将通过一个方便用户的网络界面（目标4）分发给从业人员， 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软件包。 1