On ratio measures of heterogeneity for meta-analyses

Ranibizumab for the treatment of retinopathy of prematurity: systematic review and meta-analysis

Anti-VEGF drugs like ranibizumab can be used to treat retinopathy of prematurity (ROP) by halting the formation of abnormal blood vessels, or lasers can be used to burn the edges of the retina where these vessels are present. The objective is to compare the efficacy for ROP between ranibizumab and laser therapy.

Material and methods

Electronic searches will be carried out in medical databases with key words and controlled vocabulary terms. Randomized controlled trials (RCT) will be assessed. The primary outcome will be the full ROP regression. Two reviewers will extract the data using predefined forms and, to assess the quality of the study, we will use RoB 2.0, the tool for randomized controlled trials developed by the Cochrane Collaboration. We used a combination of the inverse-variance approach and random-effects models for the meta-analysis.

Results

The eyes of 182 preterm infants who had ranibizumab treatment were assessed in a total of 364 eyes, and 135 infants received laser therapy. The follow-up period was between 6 and 24 months. Ranibizumab was not associated with greater regression of ROP compared to laser therapy in preterm infants (RR: 1.09, CI 95%: 0.95-1.24; p: 0.22). Also, ranibizumab was not associated with recurrence of ROP compared to laser therapy in preterm infants (RR: 3.77, CI 95%: 0.55-25.81; p: 0.22).

Conclusions

The efficacy of ranibizumab compared to laser is very uncertain in terms of ROP regression and decreased ROP recurrence in preterm infants.

Systematic Review Registration

identifier PROSPERO (CRD42022324150).

Quantitative evidence synthesis: a practical guide on meta-analysis, meta-regression, and publication bias tests for environmental sciences

Meta-analysis is a quantitative way of synthesizing results from multiple studies to obtain reliable evidence of an intervention or phenomenon. Indeed, an increasing number of meta-analyses are conducted in environmental sciences, and resulting meta-analytic evidence is often used in environmental policies and decision-making. We conducted a survey of recent meta-analyses in environmental sciences and found poor standards of current meta-analytic practice and reporting. For example, only ~ 40% of the 73 reviewed meta-analyses reported heterogeneity (variation among effect sizes beyond sampling error), and publication bias was assessed in fewer than half. Furthermore, although almost all the meta-analyses had multiple effect sizes originating from the same studies, non-independence among effect sizes was considered in only half of the meta-analyses. To improve the implementation of meta-analysis in environmental sciences, we here outline practical guidance for conducting a meta-analysis in environmental sciences. We describe the key concepts of effect size and meta-analysis and detail procedures for fitting multilevel meta-analysis and meta-regression models and performing associated publication bias tests. We demonstrate a clear need for environmental scientists to embrace multilevel meta-analytic models, which explicitly model dependence among effect sizes, rather than the commonly used random-effects models. Further, we discuss how reporting and visual presentations of meta-analytic results can be much improved by following reporting guidelines such as PRISMA-EcoEvo (Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Ecology and Evolutionary Biology). This paper, along with the accompanying online tutorial, serves as a practical guide on conducting a complete set of meta-analytic procedures (i.e., meta-analysis, heterogeneity quantification, meta-regression, publication bias tests and sensitivity analysis) and also as a gateway to more advanced, yet appropriate, methods.

On ratio measures of heterogeneity for meta-analyses

As a measure of heterogeneity in meta-analysis, the coefficient of variation (CV) has been recently considered, providing researchers with a complement to the very popular I 2 measure. While I 2 measures the proportion of total variance that is due to variance of the random effects, the CV is the ratio of the standard deviation of the random effects to the effect of interest. Consequently, the CV provides a different measure of the extent of heterogeneity in a meta-analysis relative to the effect being measured. However, very large CV values can occur when the effect is small making interpretation difficult. The purpose of this article is two-fold. First, we consider variants of the CV that exist in the interval 0 , 1 which may be preferable for some researchers. Second, we provide interval estimators for the CV and its variants with excellent coverage properties. We perform simulation studies based on simulated and real data sets and draw comparisons between the methods. For both the CV and its transformations, we recommend confidence intervals using the propagating imprecision method or, as a simpler alternative but at the expense of slightly worse performance in terms of coverage, combining reduced-coverage confidence intervals for the two parameters. These interval estimators typically have better coverage properties for the CV measure than those previously considered.