Joint Meta-Regression Methods Accounting for Postrandomization Variables

考虑后随机变量的联合元回归方法

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.