Effect moderation and heterogeneity in meta-analysis: a meta-regression-based reinterpretation of surgical site infection risk factors after craniotomy and cranioplasty

Why clinicians should know about it

• Picked for Neurosurgery (top studies of the week, 2 August 2026).

Abstract

INTRODUCTION: The presence of effect moderation can compromise the interpretation of meta-analyses. Hence, this characteristic has potential clinical relevance. One tool to identify effect moderation is meta-regression; however, this approach is seldom used. Meta-regression incorporates study-level characteristics to explore whether such characteristics are associated with variation in effect estimates. We applied meta-regression to examine a significant problem in neurosurgery: surgical site infection after craniotomy and cranioplasty. Numerous meta-analyses have attempted to synthesize data from individual studies that have sought to identify infection risk factors. Nevertheless, the results have been equivocal, and these meta-analyses never applied meta-regression. In this exploratory study, we hypothesized that meta-regression might provide insight into the interpretation of prior meta-analyses. METHODS: We extracted data on 18 parameters from studies included in four meta-analyses. A modified GRADE tool was used to assess the certainty of the estimates of infection incidence in each study. We examined bubble plots to assess univariate associations between these parameters and surgical site infection. RESULTS: Population size was found to be an effect moderator; the smaller the study population, the greater the effect estimate (confirmed in a sensitivity analysis using multivariable modelling). Therefore, we determined whether the interpretation of forest plots in published meta-analyses would change if analyses were stratified by population size. We used stratification thresholds of 500, 750, and 1,000. The interpretation of forest plots changed for four potential risk factors at different stratification thresholds. Male sex and implant use became associated with increased infection. An increase in the proportion of trauma cases was associated with a reduced incidence of infection, while emergency surgery shifted from an association to no association. CONCLUSION: Meta-regression provides a complementary interpretive framework that can alter conclusions drawn from meta-analyses by identifying effect moderators and by applying stratified analysis.

Abstract as published, via PubMed.