Joint Meta-Regression Methods Accounting for Postrandomization Variables

统计后随机化变量的联合Meta回归方法 主要研究员：朱海涛，医学博士，博士。 摘要 对比较有效性研究和循证医学的兴趣迅速增长，导致了 极大地增加了对系统评价和荟萃分析的关注，它们综合和对比了多个 大量随机临床试验。T O检查协变量对研究特定治疗效果的影响，Meta- 回归方法可用于比较两种处理的常规荟萃分析和网络 同时比较多个处理的Meta分析 。 虽然在方法上达成了广泛的共识 检查研究水平的协变量，这些协变量由于随机化而在研究的治疗臂中是相似的 针对性别分化后的变量进行调整更具挑战性，预计 一项研究中的治疗武器。例如，差异不遵从性，以 过早停止或退出治疗，失去随访，或改用替代疗法。发送到 就我们所知，现有的Meta回归方法只关注 研究水平协变量的影响， 它们被假定是固定的，而后随机化变量通常被认为是随机的。因此，前- 上市的Meta回归方法不能解释随机化后的变量。 因为后随机化变量，如差异不遵从性，会在估计 治疗计划的效果，在回应PA-16-161这项建议的总体目标是发展尖端关节 解释Meta分析中的后随机化变量的模型，并将它们集成到公开可用的， 易于使用的软件，以提高元分析的重复性、有效性和概括性。具体来说， 我们将在以下三个具体目标中应用贝叶斯分层模型：1)开发联合Meta回归方法。 对传统Meta分析中的随机化变量进行调整；2)发展多变量联合Meta分析 网络Meta分析中调整后化变量的回归方法；3)客观评价网络Meta分析 对所提出的方法进行评估，并开发一个开源的R包。 我们将评估这些方法与现有的荟萃分析方法的优缺点。 消耗臭氧层物质，通过实际数据应用和广泛的模拟。拟议的统计方法将广泛地 适用于多种荟萃分析。完成这些目标将大大提高比较效果 通过创新的荟萃分析方法进行研究和循证医学。它将改善公众健康 通过促进各种癌症以及心血管、传染病和其他疾病的治疗选择。

Joint Meta-Regression Methods Accounting for Postrandomization Variables Principal Investigator: Haitao Chu, M.D., Ph.D. Summary The rapid growth of interest in comparative effectiveness research and evidence-based medicine has led to dramatically increased attention to systematic reviews and meta-analyses, which synthesize and contrast multi- ple randomized clinical trials. T o examine the impact of covariates on study-specific treatment effects, meta- regression methods are available for conventional meta-analysis comparing two treatments and for network meta-analysis simultaneously comparing multiple treatments . While there is broad consensus on methods for examining study-level covariates  which are similar across a study's treatment arms because of randomization  it is much more challenging to adjust for postrandomization variables, which are expected to differ between treatment arms within a study. Examples include differential noncompliance, measured as the proportion of premature treatment discontinuation or drop out, loss to follow-up, or change to an alternative therapy. To the best of our knowledge, existing meta-regression methods only focus on the impact of study-level covariates, which are assumed to be fixed, while postrandomization variables are generally considered random. Thus, ex- isting meta-regression methods cannot account for postrandomization variables. Because postrandomization variables such as differential noncompliance can induce bias in estimating the effect of treatment plans, in responding to PA-16-161 this proposal's overall goal is to develop cutting-edge joint models to account for postrandomization variables in meta-analysis, and to integrate them into publicly available, easy-to-use software to enhance the reproducibility, validity, and generalizability of meta-analyses. Specifically, we will apply Bayesian hierarchical models in these three specific aims: 1) develop joint meta-regression meth- ods to adjust for postrandomization variables in conventional meta-analysis; 2) develop multivariate joint meta- regression methods to adjust for postrandomization variables in network meta-analysis; and 3) objectively eval- uate the proposed methods and develop an open-source R package. We will evaluate the strengths and weaknesses of these methods compared to existing meta-analysis meth- ods, through real data applications and extensive simulations. The proposed statistical methods will be broadly applicable to many meta-analyses. Completing these aims will substantially advance comparative effectiveness research and evidence-based medicine through innovative meta-analysis methods. It will improve public health by facilitating treatment selection for various cancers and for cardiovascular, infectious, and other diseases.