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No comprehensive statistical assessment of publication bias has been conducted in remdesivir-based intervention research for COVID-19 patients. This study aims to examine all meta-analyses of the efficacy of remdesivir interventions in COVID-19 patients and perform a statistical assessment of publication bias.

This is an analytic study conducted to assess the impact of publication bias on the results of meta-analyses of remdesivir-based interventions in patients infected with COVID-19. All English full-text meta-analyses published in peer-reviewed journals in 2019–2021 were included. A computerized search of PubMed and Web of Science electronic databases was performed on December 24, 2021. The trim-and-fill method calculated the number of missing studies and the adjusted cumulative effect sizes.

The final analysis comprised 21 studies with 88 outcomes. The investigation revealed missing studies in 46 outcomes (52%). Seventy-six missing studies were replaced in the outcomes using the trim-and-fill procedure. The adjusted recalculated effect sizes of the 27 outcomes increased by an average of 0.04. In comparison, the adjusted effect size of 18 outcomes fell by an average of 0.036. Only 14 out of 46 outcomes with publication bias were subjected to a gray literature search (30%). To discover related research, no gray literature search was conducted in most outcomes with publication bias (n = 32; 70%). In conclusion, the reported effect estimates regarding the effect of remdesivir in COVID-19 patients are only slightly affected by publication bias and can be considered authentic. Health-care decision-makers in COVID-19 should consider current research results when making clinical decisions.

Most health decisions are based on the effect sizes revealed in meta-analyses. When deciding on remdesivir-based treatment for COVID-19 patients, therefore, the outcomes of this investigation may be of paramount importance to health policymakers, leading to better treatment strategies.

According to the results, no major publication bias and missing studies were detected on average. Therefore, the calculated effect sizes of remdesivir-based interventions on meta-analyses can be used as authentic and unbiased benchmarks by health-care decision-makers in treating patients with COVID-19.

This is the first study to examine the effect of publication bias and gray literature searches on the results of meta-analyses of treatment with COVID-19 (remdesivir).