Statistical methods for clinical trials with multivariate longitudinal outcomes

 描述(由申请人提供)：在纵向收集多变量结果数据的临床试验中确定治疗效果是一个困难而开放的问题。数据的异质性、结果的尺度、缺失的数据以及同一受试者的结果内部和之间的相关性使问题变得更加复杂。为了解决这一问题，本项目建议开发多维潜在特质线性混合模型(MLTLMM)，定义治疗效果，并建立必要的模型复杂性，以纳入可能导致治疗效果估计中强烈偏差的主要数据成分。该提案的总体目标是：1)开发一个用于分析多变量纵向数据的建模框架，并建立一类越来越复杂的模型，以解决数据中已知的和目前被忽略的问题；2)提供快速推理和统计原则性的推理方法；3)开发一类用于建模选择的敏感性分析方法；4)开发个性化动态预测工具，以促进靶向治疗；5)将这些方法应用于当前临床试验的数据；以及6) 通过专业软件开发和网络部署来开发和标准化新提出的方法。我们在多变量纵向数据中定义和估计总体治疗效果的方法满足了许多试验的关键需求 具有类似数据结构的医疗条件(例如，阿尔茨海默病、亨廷顿病)。

 DESCRIPTION (provided by applicant): Defining the treatment effects in clinical trials that collect multivariate outcome data longitudinally is a difficult and open problem. The problem is further complicated by the heterogeneity of data, outcome scales, missing data, and correlation within and between outcomes of the same subject. To address this problem, this project proposes to develop the multidimensional latent trait linear mixed model (MLTLMM), define the treatment effect, and build the necessary complexity of the model to incorporate the major components of the data that could lead to strong biases in treatment effect estimation. The overall objectives of this proposal are to: 1) develop a modeling framework for analyzing multivariate longitudinal data and build an increasingly more sophisticated class of models that account for known, and currently ignored, problems in the data; 2) provide fast inferential and statistically principled approaches to inference; 3) develop a class of sensitivity analysis approaches to modeling choices; 4) develop tools for personalized dynamic predictions to facilitate targeted treatments; 5) apply these methods to data from current clinical trials; and 6) develop and standardize the newly proposed approaches via professional software development and web deployment. Our methods of defining and estimating the overall treatment effects in multivariate longitudinal data address the critical need across trials of many medical conditions (e.g., Alzheimer's disease, Huntington's disease) with a similar data structure.