Joint Meta-Regression Methods Accounting for Postrandomization Variables

考虑随机化后变量的联合元回归方法

• 批准号: 9431714
• 负责人: Haitao Chu
• 金额: $ 21.14万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-05 至 2019-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9431714
• 关键词: Accounting; Address; Alternative Therapies; Attention; Attenuated; Bayesian Modeling; Benefits and Risks; Biochemical Markers; Cardiovascular system; Clinical; Communities; Computer software; Consensus; Data; Data Analyses; Development; Disease; Doctor of Medicine; Doctor of Philosophy; Dropout; Drops; Evidence Based Medicine; Goals; Healthcare; Individual; Investigation; Joints; Malignant Neoplasms; Manuscripts; Measures; Meta-Analysis; Methods; Modeling; Outcome Measure; Patients; Pattern; Phase; Principal Investigator; Property; Public Health; Publishing; Randomized; Randomized Clinical Trials; Reproducibility; Research Personnel; Scientist; Selection for Treatments; Source; Statistical Methods; Withdrawal; arm; comparative effectiveness; effectiveness research; evidence base; experience; follow-up; improved; innovation; interest; non-compliance; open source; pill; premature; primary outcome; rapid growth; simulation; software development; systematic review; theories; treatment effect; treatment planning; treatment response; user friendly software

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.

关节元回归方法考虑了授异构化变量 首席研究员：Haitao Chu，M.D。，博士 概括 比较有效性研究和循证医学的兴趣快速增长导致 大大增加了对系统评价和荟萃分析的关注，它们综合和对比多 PLE随机临床试验。 o检查协变量对特异性治疗效应的影响 回归方法可用于比较两种处理和网络的常规荟萃分析 荟萃分析仅比较多种治疗 。 虽然对方法有广泛的共识 研究级别的协变量，由于随机化而在研究的治疗组中相似 调整后变量的挑战要多得多，预期之间会有所不同 一项研究中的治疗臂。示例包括差异不合规，以衡量的比例来衡量 过早的治疗停药或退学，损失进行随访或更改为替代疗法。到 最好的知识，现有的元回归方法仅关注 研究级协变量的影响， 假定这些是固定的，而后施的变量通常被认为是随机的。那就去了 iSATIT元回归方法无法解释Postrandomization变量。 因为诸如差异不符合差异之类的后变量可能会引起偏见 治疗计划的影响，在回应PA-16-161时，该提案的总体目标是开发最先进的联合 在荟萃分析中解释跨汇聚变量的模型，并将它们整合到公开可用的情况下， 易于使用的软件，可增强荟萃分析的可重复性，有效性和概括性。具体来说， 我们将在这三个特定目的中应用贝叶斯分层模型：1）发展关节元回归甲基 在常规荟萃分析中调整后分析变量的OD； 2）发展多元关节元 回归方法以调整网络荟萃分析中的后体变量； 3）客观评估 - UATE提出的方法并开发一个开源R软件包。 与现有的荟萃分析相比，我们将评估这些方法的优势和劣势 ODS，通过实际数据应用程序和广泛的模拟。提出的统计方法将广泛 适用于许多荟萃分析。完成这些目标将大大提高比较效率 通过创新的荟萃分析方法研究和循证医学。它将改善公共卫生 通过支持各种癌症以及心血管，感染和其他疾病的治疗选择。