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）基本上依赖于对治疗有效性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。针对特定病症的治疗选择越来越多，以及其成本的快速上升，使得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）也被称为混合或多重治疗荟萃分析，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，扩大了传统成对荟萃分析的范围。与传统的仅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，一个典型的随机试验只比较了几种（通常是两种）治疗方法，这在本质上造成了大量的缺失数据，比如说，当必须同时比较十几种治疗方法时，因为在特定试验中没有研究的治疗方法的结果是设计缺失的。目前可用的统计方法，这是基于治疗对比，只侧重于相对治疗效果的估计，并有其他严重的局限性。 该提案的总体目标是开发尖端的统计方法，并且 将它们整合到公开可用的易于使用的软件中，以增强以患者为中心的NMA。具体来说，我们将从缺失数据方法的角度开发二元结果的多变量贝叶斯分层模型，具有以下三个具体目标：1）扩展我们在估计以患者为中心的参数（例如，绝对风险、风险差和相对风险），具有单一终点，以允许不可验证的缺失; 2） 同时对多个终点（例如有效性和安全性结局）进行建模，并适当考虑不可重复的缺失; 3）纳入个体患者特征。此外，我们提出了新的方法来测量和检测直接和间接证据之间的不一致性，并谨慎地从不太可靠的数据源中借用力量。 我们建议通过许多真实的数据应用和模拟来对这些方法的优点和缺点进行实证评估。这三个目标的实现将大大推进CER分析方法，以比较多个终点的多种治疗方法，并根据患者特征进行定制。

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.