Joint Meta-Regression Methods Accounting for Postrandomization Variables

考虑后随机化变量的联合元回归方法 主要研究者：Haitao Chu，M.D.，博士 总结 对比较有效性研究和循证医学的兴趣迅速增长， 极大地增加了对系统综述和荟萃分析的关注，这些综述和荟萃分析综合和对比了多个 多项随机临床试验。不 检查协变量对研究特定治疗效果的影响，Meta分析 回归方法可用于比较两种治疗的常规荟萃分析和网络分析。 荟萃分析同时比较多种治疗 . 虽然在方法上存在广泛共识， 检查研究水平的协变量，由于随机化，这些协变量在研究的治疗组中相似 然而，调整随机化后的变量更具挑战性，这些变量预计在 研究中的治疗组。例子包括不同的不遵守，衡量的比例， 提前停止治疗或脱落、失访或改用替代治疗。到 据我们所知，现有的元回归方法只关注 研究水平协变量的影响， 假设其是固定的，而随机化后的变量通常被认为是随机的。因此，前- 多元回归方法不能解释随机化后的变量。 由于随机化后变量（如差异性不依从性）可能会导致估计 治疗计划的效果，在回应PA-16-161这一建议的总体目标是发展尖端联合 模型来解释荟萃分析中的随机化后变量，并将其整合到公开可用的， 易于使用的软件，以提高荟萃分析的再现性，有效性和普遍性。具体地说， 我们将在这三个具体目标中应用贝叶斯层次模型：1）开发联合元回归方法， 在传统荟萃分析中调整随机化后变量; 2）开发多变量联合Meta， 回归方法，以调整网络荟萃分析中的随机化后变量;和3）客观地评估， 评估所提出的方法，并开发一个开源的R包。 我们将评估这些方法与现有荟萃分析方法相比的优势和劣势。 ods，通过真实的数据应用和广泛的模拟。拟议的统计方法将广泛 适用于许多荟萃分析。完成这些目标将大大提高相对有效性 研究和循证医学通过创新的荟萃分析方法。它将改善公众健康 通过促进各种癌症和心血管、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.