Bayesian Methods and Software for Patient-Centered Network Meta-Analysis of Binar

描述（由申请人提供）：比较有效性研究（CER）从根本上依赖于对治疗疗效和安全性的准确评估，理想情况下，可以针对特定患者进行量身定制。针对特定疾病的治疗选择越来越多，其成本也迅速上升，因此越来越需要在临床实践中对多种治疗进行科学严格的同时比较。网络荟萃分析（NMA）也被称为混合或多种治疗荟萃分析，通过同时分析随机对照试验中干预措施的直接比较和试验间的间接比较，扩大了传统两两荟萃分析的范围....与传统的只有两种治疗的荟萃分析相比，NMA提出了许多额外的统计挑战。特别是，一项典型的随机试验只比较几种（通常是两种）治疗方法，当必须同时比较12种治疗方法时，这本质上造成了大量缺失数据，因为在特定试验中未研究的治疗方法的结果被设计为缺失。目前可用的统计方法基于治疗对比，只关注相对治疗效果的估计，并且有其他严重的局限性。这项提案的总体目标是发展尖端的统计方法，以及

DESCRIPTION (provided by applicant): Comparative effectiveness research (CER) relies fundamentally on accurate assessment of treatment efficacy and safety that, ideally, can be tailored to specific patients. The growing number of treatment options for a given condition, as well as the rapid escalation in their costs, has generated an increasing need for scientifically rigorous simultaneous comparisons of multiple treatments in clinical practice. Also called mixed or multiple treatments meta-analysis, network meta-analysis (NMA) expands the scope of a conventional pairwise meta-analysis by simultaneously analyzing both direct comparisons of interventions within randomized controlled trials and indirect comparisons across trials .... Compared to traditional meta-analysis of just two treatments, NMA presents many additional statistical challenges. In particular, a typical randomized trial compares only a few (typically tw) treatments, which intrinsically creates a large amount of missing data when, say, a dozen treatments must be compared simultaneously, since the outcomes for treatments not studied in a particular trial are missing by design. Currently available statistical methods, which are based on treatment contrasts, focus only on relative treatment effect estimates and have other serious limitations. The overall goal of this proposal is to develop cutting-edge statistical methods, and to integrate them into publicly available, easy-to-use software, to enhance patient-centered NMA. Specifically, we will develop multivariate Bayesian hierarchical models for binary outcomes from the perspective of missing data methods with the following three specific aims: 1) to extend our preliminary work on estimating patient-centered parameters (e.g., absolute risk, risk difference and relative risk) with a single endpoint to allow non-ignorable missingness; 2) to simultaneously model multiple endpoints (e.g. outcomes for efficacy and safety) with proper consideration of non-ignorable missingness; and 3) to incorporate individual patient characteristics. In addition, we propose new methods to measure and detect inconsistency between the direct and indirect evidence, and to borrow strength cautiously from less reliable data sources. We propose to perform empirical assessment of the strengths and weaknesses of these methods through many real data applications and simulations. Completion of the three aims will substantially advance CER analytical methods for comparing multiple treatments across multiple endpoints and tailored to patient characteristics.