ArticleBMC medical research methodology2025
Evaluating the properties of the fragility index of meta-analyses.
Article in BMC medical research methodology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Magnetic Resonance Imaging-Based Artificial Intelligence in Predicting Prostate Cancer Biochemical Recurrence: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
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Authors and funding
4 authors.
Funding
Abstract
backgroundThe fragility index (FI) has become an increasingly popular supplementary measure for evaluating the robustness of a study's conclusions. While initially developed for individual clinical trials, the FI has been extended to meta-analyses (MAs) of multiple studies. However, the existing literature provides limited insights into the properties of the FI in the context of MAs. This study aims to explore various statistical methods for MAs and assess the improvement in FI of MAs compared to the individual studies they comprise.
methodsWe investigated the empirical distributions of FI and fragility quotient (FQ) using a large database of Cochrane MAs with binary outcomes. The FI of MAs was calculated under different statistical methods, including fixed-effect and random-effects models, with between-study variance estimators (restricted maximum-likelihood and DerSimonian-Laird), alongside Hartung-Knapp-Sidik-Jonkman (HKSJ) confidence interval adjustments. Subgroup analyses were performed to explore the impact of heterogeneity, sample size, and effect measures on fragility. Furthermore, we employed a metric to evaluate the improvement in fragility by comparing the FI of MAs with the FIs of the individual studies they included.
resultsThe median FI was 5 (IQR: 2-11) among 3,758 MAs analyzed, with 29% reporting statistically significant results. Notably, 15% of MAs had an FI of 1, and 54% had an FI ≤ 5. MAs with larger sample sizes or higher [Formula: see text] values, tended to exhibit greater robustness. HKSJ adjustments introduced more uncertainty, yielding more fragile results compared to analyses without these adjustments. Fragility improvement was higher in MAs with considerable heterogeneity.
conclusionsThis study highlights the variability in fragility across MAs and underscores the influence of heterogeneity and statistical methods on FI. Further research is warranted to refine the assessment of fragility and incorporate clinical relevance into these evaluations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.