Robust Bayesian Model Averaging Meta-Analysis of Menstrual Disorders in COVID-19 Survivors: A Methodological Meta-Analysis Study.

Background and Aims: The COVID-19 pandemic has significantly affected public health worldwide. This study applies a novel Robust Bayesian Model Averaging-Publication Selection Model Averaging method (RoBMA-PSMA) to address publication bias for a single proportion and estimate the pooled prevalence of menstrual disorders in surviving women from SARS-CoV-2 through a meta-analysis of existing evidence. Methods: Data analysis was performed using both classical and novel RoBMA-PSMA approaches for meta-analysis. The R software 4.4.1 package "metafor" and JASP 0.18.3 software were used to conduct statistical analysis. Results: The pooled prevalence estimates via conditional ROBMA-PSMA, were: amenorrhea, 12% (95% CI: 3%-20%); intermenstrual bleeding, 17% (95% CI: 3%-31%); menstrual cycle regularity changes, 24% (95% CI: 9%-34%); menstrual duration changes, 15% (95% CI: 3%-32%); menstrual volume changes, 12% (95% CI: 2%-24%), pain related changes, 17% (95% CI: 3%-30%), and overall, 9% (95% CI: 5%-13%). Results of other classical methods, including random/fixed and trim and fill methods showed significantly different pooled effect sizes. Conclusion: Using a Robust Bayesian methodology, we found that 9%-24% of women of reproductive age experienced menstrual disorders during the COVID-19 pandemic, highlighting its significant impact on women's health. To address this, it is crucial to educate women about the pandemic's effects on menstrual health and provide support services, including counseling and access to specialized healthcare providers. The RoBMA-PSMA approach can help researchers effectively tackle publication bias and heterogeneity, offering a straightforward, data-driven method that requires minimal technical expertise, supported by a user-friendly tool.