Developing a Robust and Efficient Strategy for Censored Covariates to Improve Clinical Trial Design for Neurodegenerative Diseases

为删失协变量制定稳健有效的策略，以改进神经退行性疾病的临床试验设计

• 批准号: 10634043
• 负责人: Tanya Pamela Garcia
• 金额: $ 48.56万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2028-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10634043
• 关键词: Area; Behavioral; Clinical Trials; Clinical Trials Design; Cognitive; Data; Data Set; Disease; Disease Progression; Drops; Enrollment; Funding; Genetic; Huntington Disease; Intervention; Investigational Therapies; Joints; Lead; Measurement; Measures; Methods; Modeling; Modification; Motor; Neurodegenerative Disorders; Patients; Research Personnel; Science; Scientist; Source Code; Statistical Models; Symptoms; Testing; Time; United States National Institutes of Health; Visit; Work; clinical diagnosis; cohort; design; improved; longitudinal dataset; model building; neuroimaging; open source; prevent; semiparametric; theories

Project Summary: Developing disease-modifying therapies for neurodegenerative diseases has been challenging, in part because accurate statistical models to identify the optimal time for intervention do not exist. Models of how symptoms worsen over time (i.e., the symptom trajectory) before and after a clinical diagnosis can help identify that optimal time. These models can help pinpoint when a therapy could prevent a clinical diagnosis, or slow the disease after a clinical diagnosis. Yet modeling the symptom trajectory is not easy even for Huntington disease, a disease for which researchers can track symptoms in patients guaranteed to develop it. Like other neurodegenerative diseases, Huntington disease progresses slowly over decades, so studies that track symptoms often end before clinical diagnosis. This makes time to clinical diagnosis right-censored (i.e., a patient's motor abnormalities will merit a clinical diagnosis sometime after the last study visit, but exactly when is unknown), leaving researchers with the challenge of trying to model the symptom trajectory before and after clinical diagnosis without full information about when clinical diagnosis occurs. The challenge creates a unique statistical problem of modeling the symptom trajectory as a function of a right-censored covariate, time to clinical diagnosis. Tackling this problem by modeling the distribution for time to clinical diagnosis has long been thought to be the best strategy. For years, we and others worked to develop reliable distribution models, but we found that if the model is even slightly wrong, we get biased estimates of how the symptom trajectory changes as a function of time to clinical diagnosis. This bias causes problems for clinical trials because they are incorrectly powered to determine if a therapy modifies the disease course with statistical significance. We began seeking a strategy that estimates the symptom trajectory as a function of time to clinical diagnosis without needing to accurately model the distribution for time to clinical diagnosis. Our team developed such a strategy for a related problem: estimating a regression model that has a covariate measured with error. Like a right-censored covariate, when a covariate is measured with error, the covariate's true value and distribution are unknown. Rather than finding the correct distribution, our nontraditional strategy accurately estimates the regression model even when the distribution for the covariate is mismodeled. Our overarching objective is to develop a similarly robust strategy when we have a right-censored covariate, which requires tackling challenges in three new areas: noninformative censoring (Aim 1), informative censoring (Aim 2), and handling longitudinal measures of the symptom trajectory (Aim 3). Upon completion, our work will produce robust estimates of the Huntington disease symptom trajectory as a function of time to clinical diagnosis. The work is timely, given recent therapies that show potential for modifying the course of Huntington disease. Correctly powered clinical trials will enable researchers to test these therapies and determine if they modify the disease course. Our strategy could help design these clinical trials and push forward the science of Huntington disease and other neurodegenerative diseases.

项目摘要：开发针对神经退行性疾病的疾病缓解疗法一直具有挑战性，部分原因是 因为不存在用于确定最佳干预时间的准确统计模型。 在临床诊断之前和之后随着时间的推移（即症状轨迹）恶化可以帮助确定最佳时间。 这些模型可以帮助查明治疗何时可以阻止临床诊断，或在临床诊断后减缓疾病的进展。 诊断。 然而，即使对于亨廷顿病，对症状轨迹进行建模也不容易，研究人员针对这种疾病 与其他神经退行性疾病一样，亨廷顿病可以追踪患者的症状。 几十年来进展缓慢，因此跟踪症状的研究通常在临床诊断之前就结束了。 临床诊断右删失（即患者的运动异常在诊断后的某个时间值得进行临床诊断） 最后一次研究访问，但具体时间未知），这给研究人员留下了尝试模拟症状的挑战 临床诊断前后的轨迹没有关于临床诊断何时发生的完整信息。 创建了一个独特的统计问题，将症状轨迹建模为右删失协变量时间的函数 达到临床诊断的目的。 通过对临床诊断时间的分布进行建模来解决这个问题长期以来一直被认为是 多年来，我们和其他人致力于开发可靠的分销模型，但我们发现，如果 模型甚至有一点错误，我们对症状轨迹如何随时间变化的估计存在偏差 这种偏差会给临床试验带来问题，因为它们无法正确确定。 如果一种疗法改变了疾病进程并具有统计学意义，我们就开始寻找一种策略来估计 症状轨迹作为临床诊断时间的函数，无需精确建模分布 我们的团队针对相关问题制定了这样的策略：估计回归模型。 其协变量的测量存在误差，就像右删失协变量一样，当协变量的测量存在误差时， 我们的非传统方法不是找到正确的分布，而是未知协变量的真实值和分布。 即使协变量的分布建模错误，策略也能准确估计回归模型。 我们的首要目标是当我们有一个右审查协变量时制定一个类似的稳健策略，该策略 需要在三个新领域提出违规挑战：非信息性审查（目标 1）、信息性审查（目标 2）和 症状轨迹的纵向处理措施（目标 3）完成后，我们的工作将产生稳健的结果。 亨廷顿病症状轨迹随临床诊断时间的变化的估计这项工作是及时的。 鉴于最近的疗法显示出改变亨廷顿病病程的潜力，正确的临床动力。 试验将使研究人员能够测试这些疗法并确定它们是否可以改变疾病进程。 帮助设计这些临床试验并推动亨廷顿病和其他神经退行性疾病的科学发展。