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.

项目概要/摘要 该提案的目标是开发统计方法来评估不同时期的治疗策略 时间点并根据患者的纵向生物标志物确定最佳治疗策略 我们的研究旨在确定慢性粒细胞白血病患者的最佳治疗时机。 白血病（CML）接受干细胞移植（SCT）可以治愈白血病，但它与生命有关。 因此，大多数患者会从其他不太激进的治疗方案开始。 安全得多，但无法治愈疾病，因此 SCT 最佳时机的决策取决于以下因素： 然而，进行随机对照试验来衡量患者的疾病进展是不可行的。 SCT 在不同时间的风险和收益，以优化这一复杂且复杂的决策过程。 需要全面的统计模型来准确估计收益和风险（以及 随着时间的推移，不同 SCT 时机选择的患者的权衡取舍 然而，这些尚未得到证实。 由于以下详述的挑战而开发。 首先，SCT 的最佳决策问题不能用单一的统计模型来回答，它 需要从一系列模型和分析中收集信息，其次，很可能没有。 这个问题有统一的解决方案，因为 SCT 的最佳时机取决于每个人的疾病 状态测试后，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方式做出治疗决定第三，每个人的治疗决定。 个人必须考虑其竞争风险，包括因治疗相关并发症和其他原因导致的死亡 最后，不可能实施最佳决策。 没有易于使用的软件，提出以下具体目标来解决这些问题。 目标 1：使用功能组件主成分分析 (FPCA) 技术来充分捕获 从患者的纵向生物标志物轨迹中找出主导模式，并将其用作预测因子 患者疾病进展的风险。 目标 2：根据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。 目标 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 计时选项下。 目标 4：进行验证研究、开发软件并扩大应用。 三项 CML 研究将用于相互验证 SCT 软件的最佳时机。 具有用户友好界面的程序将公开提供。 项目将被调整并应用于肾脏疾病的研究，以测试其广泛的应用。