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：开发神经退行性疾病的疾病改良疗法一直是挑战，部分是 因为不存在确定干预最佳时间的准确统计模型。符号的模型 临床诊断之前和之后随着时间的流逝（即症状轨迹）恶化，可以帮助确定最佳时间。 这些模型可以帮助指出何时治疗可以防止临床诊断，或者在临床后放慢疾病 诊断。 然而，即使对于亨廷顿疾病，对症状轨迹进行建模也不容易，这是研究人员的疾病 可以跟踪保证发展它的患者的症状。像其他神经退行性疾病一样，亨廷顿病 几十年来，进展缓慢，因此轨道症状通常在临床诊断之前结束。这有时间去 临床诊断右审查（即，患者的运动异常将在此后的某个时候值得 上次研究访问，但到底是什么时候），使研究人员面临试图对症状建模的挑战 临床诊断前后的轨迹，没有有关临床诊断何时发生的全部信息。挑战 创建一个独特的统计问题，即对症状轨迹建模作为右审查协变量的函数，时间 进行临床诊断。 长期以来，人们认为解决这个问题的时间长期以来一直被认为是 最好的策略。多年以来，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 模型甚至有点错，我们对症状轨迹随时间变化的变化有偏见估计 进行临床诊断。这种偏见会引起临床试验的问题，因为它们无法正确确定 如果治疗可以通过统计学意义来修饰疾病病程。我们开始寻求一种估计的策略 症状轨迹是临床诊断时间的函数，而无需准确对 临床诊断的时间。我们的团队为相关问题制定了这样的策略：估计回归模型 该协变量误差。就像右审查协变量一样，当协变量以错误测量时， 协变量的真实价值和分布未知。而不是找到正确的分布，而是我们的非传统 策略即使使协变量的分布不予以编码，也可以准确估计回归模型。 我们的总体目标是当我们拥有右审查协变量时制定类似的强大策略，该策略是 需要在三个新领域应对挑战：非信息审查（AIM 1），信息审查（AIM 2）和 处理症状轨迹的纵向测量（AIM 3）。完成后，我们的工作将产生强大的 亨廷顿疾病症状轨迹的估计是临床诊断时间的函数。工作是及时的， 鉴于最近的疗法显示了改变亨廷顿疾病进程的潜力。正确动力的临床 试验将使研究人员能够测试这些疗法并确定他们是否修改了疾病病程。我们的策略可以 帮助设计这些临床试验，并推动亨廷顿疾病和其他神经退行性疾病的科学。