Using meta-analytic priors to incorporate external information for study evaluation

TL;DR

This paper demonstrates how Bayesian meta-analysis can incorporate external studies to refine estimates of risk factors for acute kidney injury in COVID-19 patients, accounting for heterogeneity.

Contribution

It introduces a method for integrating external information into primary study analysis using Bayesian meta-analysis, adjusting for heterogeneity.

Findings

Mechanical ventilation and vasopressors are confirmed as key risk factors.

External data can significantly refine effect size estimates.

Heterogeneity influences the amount of information borrowed.

Abstract

Background: The COVID-19 pandemic has had a profound impact on health, everyday life and economics around the world. An important complication that can arise in connection with a COVID-19 infection is acute kidney injury. A recent observational cohort study of COVID-19 patients treated at multiple sites of a tertiary care center in Berlin, Germany identified risk factors for the development of (severe) acute kidney injury. Since inferring results from a single study can be tricky, we validate these findings and potentially adjust results by including external information from other studies on acute kidney injury and COVID-19. Methods: We synthesize the results of the main study with other trials via a Bayesian meta-analysis. The external information is used to construct a predictive distribution and to derive posterior estimates for the study of interest. We focus on various important potential risk factors for acute kidney injury development such as mechanical ventilation, use of vasopressors, hypertension, obesity, diabetes, gender and smoking. Results: Our results show that depending on the degree of heterogeneity in the data the estimated effect sizes may be refined considerably with inclusion of external data. Our findings confirm that mechanical ventilation and use of vasopressors are important risk factors for the development of acute kidney injury in COVID-19 patients. Hypertension also appears to be a risk factor that should not be ignored. Shrinkage weights depended to a large extent on the estimated heterogeneity in the model. Conclusions: Our work shows how external information can be used to adjust the results from a primary study, using a Bayesian meta-analytic approach. How much information is borrowed from external studies will depend on the degree of heterogeneity present in the model.