Subject Area
Statistics
Abstract
Multivariate meta-analysis (MMA) and network meta-analysis (NMA) are essential tools for synthesizing evidence across multiple correlated outcomes and treatments. However, these tools face practical challenges, including outcome reporting bias (ORB), unreported within-study correlations, and computational burden. ORB can distort effect estimates in MMA, while missing within-study correlations in multivariate NMA may lead to biased conclusions. To address these challenges, this dissertation introduces two novel statistical methods. For MMA, we propose SemiMMA, a semiparametric and scalable approach that treats ORB as a missing-not-at-random problem and combines inverse propensity weighting (IPW) with the generalized method of moments (GMM). For multivariate NMA, we develop BCL-OFS, a calibrated Bayesian composite likelihood method that employs a hybrid Gibbs sampler and Open-Faced Sandwich adjustment to accommodate unreported within-study correlations without requiring a fully specified likelihood. Through extensive simulations and real-world applications, including studies of treatments for kidney disease and root coverage procedures, we demonstrate that SemiMMA yields robust estimates against ORB, while BCL-OFS produces unbiased estimates with nominal levels. These methodological advancements mitigate key biases and offer the reliability of meta-analytic conclusions in medical and public health research.
Degree Date
Summer 2025
Document Type
Dissertation
Degree Name
Ph.D.
Department
Department of Statistics and Data Science
Advisor
Yu-Lun Liu
Number of Pages
123
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Recommended Citation
Wang, Yifei, "Advancing Statistical Methods for Multivariate and Network Meta-Analysis" (2025). Statistical Science Theses and Dissertations. 51.
https://scholar.smu.edu/hum_sci_statisticalscience_etds/51
