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

ABSTRACT:

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%); menstr

SUBMITTER: Falahati G 

PROVIDER: S-EPMC12699978 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature