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.

项目摘要/摘要 该提案的目的是开发用于评估不同治疗策略的统计方法 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。我们的研究是为了确定慢性髓样患者的最佳时机的动机 白血病（CML）接受干细胞移植（SCT）。 SCT可以治愈白血病，但与生活有关 威胁风险。因此，大多数患者从其他不侵入性治疗方案开始 更安全，但无法治愈疾病。关于SCT最佳时机的决策取决于 患者病的进展。但是，进行一项随机对照试验以加权是不可行的 SCT在不同时间的风险和益处。为了优化这个决策过程，复杂的和 需要全面的统计模型来准确估计收益和风险（以及 他们的权衡）随着时间的流逝，患者在不同的SCT定时选择下。但是，这些还没有 由于下面阐述的挑战，开发了。 首先，关于SCT的最佳决定的问题无法通过单个统计模型来回答 需要从一系列模型和分析中组装信息。第二，很可能不是 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 进程状态。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 个人必须考虑其竞争风险，包括与治疗相关并发症的死亡和其他 原因（例如，心脏病和糖尿病）。最后，不可能实施最佳决策 没有易于使用的软件。提出了以下特定目标来解决这些问题。 目标1：使用功能组件主成分分析（FPCA）技术完全捕获 患者纵向生物标志物轨迹的主要模式，并将其用作预测因素 患者的疾病进展风险。 目标2：根据基线协变量和纵向生物标志物估算动态竞争风险 使用多状态模型的轨迹。 目标3：使用分析和微仿真方法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，开发软件和扩展应用。 关于SCT的最佳时机，将使用三项CML研究互相交叉验证。软件 具有用户友好界面的程序将公开可用。拟议的统计和软件 程序将适应并应用于肾脏疾病的研究以测试其广泛应用。