Estimating Long-Term Disease Trajectories from Short-Term Data

 描述（由申请人提供）：阿尔茨海默氏症临床试验设计主要是通过对预先指定组的短期数据进行分段分析来指导的。我们通过对评估分布的横截面检查来构思这些组。然后，我们描述这些组的纵向变化，以计算功效和样本量。这种方法在为分期试验建立实用的纳入标准方面是有效的，但结果标准可能是不必要的过程。新的分析方法，捕捉疾病的完整连续性将更有效地利用多变量纵向数据，允许更复杂和包容性的纳入标准，并提供新的见解，为特定的干预措施量身定制的最佳人群。更好地了解疾病动力学的长期连续性将使我们能够发现我们目前理解的差距，并提供一种全面的，数据驱动的方法来设计临床试验。支持这种新方法的AD临床试验设计的分析方法还不成熟，技术上具有挑战性。我们将开发、实施和解释新型统计方法，以准确有效地表征从临床前到痴呆的阿尔茨海默病标志物的长期动态。我们将把我们的统计方法应用于现有的短期生物标志物数据集，以优化阿尔茨海默氏症的临床试验设计，并改善个性化的预后预测。该项目将通过更准确地描述长期疾病动态，识别风险因素和理想的临床试验人群和结果指标，并提供个性化的预后预测，对我们在阿尔茨海默病方面的进展产生重大影响。具体目标1：开发一个分析框架，用于建模长期AD动态。我们将开发一种新型的分层贝叶斯潜变量模型，以从短期采样数据中识别长期多变量疾病轨迹。基础模型将扩展到使用动态贝叶斯信念网来建模抽样变异、疾病异质性和复杂网络中介结构。具体目标2：开发优化的临床试验研究设计和个性化预后。根据目标1开发的模型将用于确定研究人群和特定机制生物标志物效应的结局指标的最佳组合，并获得病程和治疗获益的个性化预测。我们将开发和验证使用我们的模型进行相关临床试验和个性化预后预测所需的机制。具体目标3：开发软件和Web应用程序。将开发实现目标1-2所述方法的计算机软件，并作为免费的R软件包分发。我们将构建一个交互式Web应用程序，以便临床医生和临床试验人员可以探索，可视化和询问我们的分析结果，以预测疾病并模拟临床试验场景。该应用程序将使临床试验人员能够根据我们的模型和他们对干预的生物标志物效应的先验知识优化他们的试验设计。

 DESCRIPTION (provided by applicant): Alzheimer's clinical trial design is predominantly guided by fragmented analyses on short-term data from pre- specified groups. We conceive these groups by cross-sectional examination of the distribution of assessments. We then characterize longitudinal changes in these groups to calculate power and sample size. This approach is effective in establishing practical inclusion criteria for staging trials, but the resuting criteria may be unnecessarily course. Novel analytical methods that capture the full continuum of the disease will make more efficient use of multivariate longitudinal data, allow more sophisticated and inclusive inclusion criteria, and provide new insights into optimal populations tailored to specific interventions. A better understanding of the long-term continuum of disease dynamics will allow us to discover gaps in our current understanding and provide a comprehensive, data-driven approach to designing clinical trials. Analytic methods to support this new approach to AD clinical trial design are underdeveloped and technically challenging. We will develop, implement, and interpret novel statistical methods to accurately and efficiently characterize the long-term dynamics of Alzheimer's disease markers from preclinical to dementia. We will apply our statistical methods to existing short-term biomarker datasets to optimize Alzheimer's clinical trial design and improve personalized prognostic predictions. This project will have a substantial impact on our progress with Alzheimer's by more accurately characterizing long-term disease dynamics; identifying risk factors and ideal clinical trial populations and outcome measures; and providing personalized prognostic predictions. Specific Aim 1: To Develop an Analytic Framework for Modeling Long-Term AD Dynamics. We will develop a novel hierarchical Bayesian latent variable model to discern long-term multivariate disease trajectories from short-term sampled data. The base model will be extended to model sampling variation, disease heterogeneity, and complex network mediation structure using Dynamic Bayesian Belief Nets. Specific Aim 2: To Develop Optimized Clinical Trial Study Designs and Personalized Prognoses. The models developed under Aim 1 will be leveraged to determine the optimal combinations of study population and outcome measure for specific mechanistic biomarker effects, and to obtain personalized predictions of disease course and therapeutic benefit. We will develop and validate the machinery necessary to use our models to make relevant clinical trial and personalized prognostic predictions. Specific Aim 3: To Develop Software and Web Application. Computer software implementing the methods developed in Aims 1-2 will be developed and distributed as a free R package. We will build an interactive web application so that clinicians and clinical trialists can explore, visualize, and interrogate our analysis results to predict prognoses and simulate clinical trial scenarios. The application will enable clinical trialists to optimize their trial designs based on our model and their prior knowledge of their intervention's biomarker effects.