Statistical methods for clinical trials with multivariate longitudinal outcomes

具有多变量纵向结果的临床试验的统计方法

• 批准号: 9030015
• 负责人: Sheng Luo
• 金额: $ 31.22万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-30 至 2019-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9030015
• 关键词: Accounting; Address; Alzheimer' s Disease; Bayesian Method; Behavioral; Clinical; Clinical Trials; Cognitive; Computer software; Data; Data Correlations; Data Set; Disease; Disease Progression; Double-Blind Method; Evaluation; Event; Future; Health; Heterogeneity; Huntington Disease; Impairment; Individual; Internet; Lead; Measures; Medical; Methodology; Methods; Modeling; Motor; Multivariate Analysis; Nature; Neurodegenerative Disorders; Online Systems; Outcome; Outcome Measure; Parkinson Disease; Patients; Performance; Phase III Clinical Trials; Placebo Control; Placebos; Randomized; Reporting; Risk; Selection for Treatments; Severity of illness; Site; Statistical Methods; Testing; Time; Translational Research; Visit; base; data structure; design; hazard; insight; interest; open source; primary outcome; prognostic tool; public health relevance; software development; time interval; tool; trait; treatment effect; user-friendly

 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.

 描述（由申请人提供）：在纵向收集多变量结局数据的临床试验中定义治疗效果是一个困难和开放的问题。数据的异质性、结果量表、缺失数据以及同一受试者结果之间的相关性使问题进一步复杂化。为了解决这个问题，本项目提出开发多维潜在性状线性混合模型（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强烈偏倚的数据的主要成分。该提案的总体目标是：1）开发用于分析多变量纵向数据的建模框架，并建立越来越复杂的一类模型，以解释数据中已知的、目前被忽视的问题; 2）提供快速推理和统计原则的推理方法; 3）开发一类敏感性分析方法以进行建模选择; 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，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解决了许多临床试验的关键需求。 医疗状况（例如，阿尔茨海默氏病、亨廷顿氏病）具有类似的数据结构。