Optimizing treatment decision by accounting for longitudinal biomarker trajectories and competing risks of each individual

通过考虑每个个体的纵向生物标志物轨迹和竞争风险来优化治疗决策

• 批准号: 10658050
• 负责人: Xuelin Huang
• 金额: $ 38.49万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-12 至 2027-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10658050
• 关键词: Accounting; Benefits and Risks; Biological Markers; Cessation of life; Chronic Disease; Chronic Kidney Failure; Chronic Myeloid Leukemia; Computer software; Data; Decision Making; Development; Diabetes Mellitus; Dimensions; Disease; Disease Progression; Future; Goals; Health Status; Heart Diseases; Individual; Kidney Diseases; Life; Literature; Malignant Neoplasms; Mean Survival Times; Measurement; Methods; Modeling; Monitor; Patient risk; Patients; Pattern; Performance; Physicians; Population; Principal Component Analysis; Process; Randomized, Controlled Trials; Research; Risk; Risk Estimate; Selection for Treatments; Series; Statistical Methods; Statistical Models; Stem cell transplant; Techniques; Testing; Time; Visit; Weight; aggressive therapy; design; flexibility; follow-up; frailty; graft vs host disease; high dimensionality; improved; leukemia; optimal treatments; predictive modeling; prognostic model; programs; risk prediction; simulation; software development; temporal measurement; treatment optimization; treatment strategy; user-friendly; validation studies; vector; virtual; virtual patient

Project Summary/Abstract The goal of this proposal is to develop statistical methods for evaluating treatment strategies at different time points and identifying optimal treatment strategies on the basis of patients' longitudinal biomarker measurements. It is motivated by our research on identifying the best timing for patients with chronic myeloid leukemia (CML) to receive a stem cell transplant (SCT). SCT can cure leukemia, but it is associated with life- threatening risks. For this reason, most patients start with other less-aggressive treatment options that are much safer but cannot cure the disease. Thus the decision-making about optimal timing of SCT depends on a patient's disease progression. However, it is infeasible to conduct a randomized controlled trial to weigh the risks and benefits of SCT at various times. To optimize this decision-making process, sophisticated and comprehensive statistical models are needed to provide an accurate estimation of the benefits and risks (and their trade-offs) over time for patients under different SCT timing options. However, these have not yet been developed, due to the challenges elaborated below. First, the question of an optimal decision on SCT cannot be answered by a single statistical model, it requires assembling information from a series of models and analyses. Second, there most likely is not a uniform solution for this question, as the optimal timing of SCT depends on each individual's disease progression status. Consequently, physicians must use patients' longitudinal biomarker trajectories to monitor their health status and make treatment decision in a dynamic fashion. Third, the treatment decision for each individual must account for their competing risks, including death by treatment-related complications and other causes (e.g., heart diseases and diabetes). Finally, it is impossible to implement optimal decision-making without an easy-to-use software. The following specific aims are proposed to solve these problems. Aim 1: Use functional component principal component analysis (FPCA) techniques to fully capture the dominant patterns from patients' longitudinal biomarker trajectories, and use them as predictors of patients’ risk of disease progression. Aim 2: Estimate dynamic competing risks based on baseline covariates and longitudinal biomarker trajectories using multi-state models. Aim 3: Use analytic and microsimulation approaches to estimate and compare the mean survival times under different SCT timing options. Aim 4: Conduct validation studies, develop software, and broaden application. Three CML studies will be used to cross-validate each other regarding the optimal timing of SCT. Software programs with user-friendly interfaces will be made publicly available. The proposed statistical and software programs will be adapted and applied to a study of kidney disease to test their broad application.

项目总结/摘要 该提案的目标是开发统计方法，用于评估不同年龄段的治疗策略。 时间点，并根据患者的纵向生物标志物确定最佳治疗策略 测量.它的动机是我们的研究确定最佳时机的慢性髓系白血病患者 白血病（CML）接受干细胞移植（SCT）SCT可以治愈白血病，但它与生命有关- 威胁风险。出于这个原因，大多数患者开始与其他侵略性较小的治疗选择， 安全得多，但不能治愈疾病。因此，关于SCT最佳时机的决策取决于 患者的疾病进展。然而，进行随机对照试验来权衡 SCT在不同时期的风险和益处。为了优化这一决策过程， 需要全面的统计模型来提供对获益和风险的准确估计（以及 他们的权衡）随着时间的推移为患者在不同的SCT定时选项。然而，这些尚未被 由于下文所述的挑战， 首先，SCT的最佳决策问题不能通过单一的统计模型来回答， 需要从一系列模型和分析中收集信息。其次，很可能没有一个 对于这个问题的统一解决方案，因为SCT的最佳时机取决于每个人的疾病 进展状态。因此，医生必须使用患者的纵向生物标志物轨迹来监测 他们的健康状况，并以动态的方式做出治疗决定。第三，每个人的治疗决定 个人必须说明他们的竞争风险，包括治疗相关并发症和其他 原因（例如，心脏病和糖尿病）。最后，无法实现最优决策 没有一个简单易用的软件。为解决这些问题，提出了以下具体目标。 目的1：使用功能成分主成分分析（FPCA）技术，以充分捕捉 从患者的纵向生物标志物轨迹的主导模式，并使用它们作为预测 患者疾病进展的风险。 目的2：基于基线协变量和纵向生物标志物估计动态竞争风险 使用多状态模型的轨迹。 目的3：使用分析和微观模拟方法来估计和比较平均生存时间 在不同的SCT定时选项下。 目标4：进行验证研究，开发软件，并扩大应用。 三项CML研究将用于交叉验证SCT的最佳时间。软件 将向公众提供界面友好的程序。拟议的统计和软件 程序将被改编并应用于肾脏疾病的研究，以测试其广泛的应用。