Fragility Index of Recently Published Meta-Analyses in Pediatric Urology: A Striking Observation

Abstract

Background and objectiveFragility Index (FI) of meta-analyses determines their stability in terms of the level of confidence and strength behind the results depicted by them. The present study was conducted to estimate the FI of recently published meta-analyses in the Journal of Pediatric Urology (JPUrol). MethodTwenty recently published articles on meta-analyses were screened to identify the eligible ones. The baseline data of each meta-analysis including the details of the author, number of included studies, total sample size, the total number of events, the status of the overall outcome (significant or non-significant), type of effect measure, type of method used for pooling the estimates, and type of effects model were recorded. FI was calculated by doing each single status modification. The 95% CI of the treatment effect was re-calculated until the statistical significance of the meta-analysis was reversed. ResultsA total of seven articles incorporating 22 meta-analyses were included. Seven (32%) of them had a statistically significant outcome prior to FI estimation. The risk ratio (17/22; 77%) was the most commonly used effect measure. The random-effects model (15/22; 68%) and the Mantel-Haenszel method (20/22; 91%) of pooling the estimates were utilized in the majority of meta-analyses. The median (Q1-Q3; range) FI of statistically significant, non-significant, and total meta-analyses were 5 (3-19.5; 2-39), 5 (3.5-6; 1-17), and 5 (3-13; 1-39) respectively. FI of ≤5 was noticed in four out of seven (57%), 9/15 (60%), and 13/22 (59%) of these meta-analyses respectively. ConclusionBased on our findings, the majority of the recently published meta-analyses in the field of pediatric urology are fragile and depend upon the event status of ≤5 participants.

Significance of fragility index in meta-analysis

“…[ 2 ] FI for meta-analysis is the minimum number of patients from one or more trials that must be included in the meta-analysis in order for a change in the event status (such as turning events into nonevents or nonevents into events) to have a significant impact on the statistical significance of the pooled effect or vice versa. [ 3 ]…”