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/项目摘要 移植等手术治疗通常对风险预测提出相当大的分析挑战 为了死亡率。例如，儿科急性肝衰竭 (PALF) 呼叫的疾病预后和治疗决策 寻找预测死亡风险的可靠工具。然而，该预测工具的发展受到以下因素的阻碍： 高频率的肝移植（LTx），其发生改变了患者的病程 并独立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。认识到多个纵向生物标志物的重要预后价值 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 开发、实施和应用合理且计算上可行的建模、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来解决依赖关系： 通过聚合多个纵向和基线协变量的信息来区分死亡和 LTx。什么时候 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 生物标志物可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。方法 评估累积发生率预测和边际概率预测的预测性能 从各个角度确定并提高预测性能。我们还制定正式的测试程序 检测不同亚型患者之间潜在的预测异质性。此外，我们建议（目标 3) 在因果推理框架下检验 LTx 效益的统计程序，适应受试者 为个性化 LTx 决策提供信息的具体好处。所有统计方法都将通过严格论证 广泛的模拟研究、敏感性分析和理论推导，以确保其理论的严谨性和 实际用处。这些方法将系统地应用于最近的 PALF 注册数据库。最终的 预测工具将通过用户友好的网络界面传播给从业者（目标 4），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领域 临床研究并将为更广泛的研究社区开发 R 包。 1