Statistical Methods and Software for Multivariate Meta-analysis

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Statistical Methods and Software for Multivariate Meta-analysis Principal Investigator: Haitao Chu, M.D., Ph.D. Summary Comparative effectiveness research (CER) aims to inform health care decisions concerning the benefits and risks of different prevention strategies, diagnostic instruments and treatment options. A meta-analysis (MA) is a statistical method that combines results of multiple independent studies to improve statistical power and to reduce certain biases within individual studies. MA also has the capacity to contrast results from different studies and identify patterns and sources of disagreement among those results. While many statistical methods for MA have been proposed and investigated, important research gaps remain. The increasing number of prevention strategies, assessment instruments and treatment options for a given disease condition, as well as the rapid escalation in costs, have generated a need to simultaneously compare multiple options in clinical practice using innovative and rigorous multivariate MA methods. Following the NIH strategic plan for data science and the National Library of Medicine priority area on “integration of heterogeneous data types”, in response to PA-18-484, this proposal's overall goal is to develop cutting-edge statistical methods to enhance the reproducibility, efficiency and generalizability of MA, as well as to develop easy-to-use software. Specifically, in this proposal, we will: (1) examine the performance of skewness of the standardized deviates for quantifying publication bias in univariate MA, and develop methods quantifying publication bias in multivariate MA; (2) develop a Bayesian hierarchical summary receiver operating characteristic (HSROC) network meta-analysis framework for simultaneously comparing multiple diagnostic tests; (3) develop a causal inference framework accounting for post-randomization variables in multivariate MA; and (4) develop open-source, cross-platform, publicly available and easy-to-use software (including R packages and SAS macros) to implement the proposed MA methods. We will evaluate the strengths and weaknesses of these proposed methods versus existing MA methods using many real case studies and extensive simulation studies. The proposed statistical methods will be broadly applicable to meta-analysis. Completing these four aims will directly benefit the CER evidence base by providing state-of-the-art methods implemented in user-friendly software including R packages and SAS macros, which will be made freely available to the public. It will improve public health by facilitating prevention, diagnosis, and treatment of cancers and cardiovascular, infectious, and other diseases.