Empirical assessment of fragility index based on a large database of clinical studies in the Cochrane Library

The fragility index (FI) and fragility quotient (FQ) are increasingly used measures for assessing the robustness of clinical studies with binary outcomes in terms of statistical significance. The FI is the minimum number of event status modifications that can alter a study result's statistical significance (or nonsignificance), and the FQ is calculated as the FI divided by the study's total sample size. The literature has no widely recognized criteria for interpreting the fragility measures' magnitudes. This article aims to provide an empirical assessment for the FI and FQ based on a large database of clinical studies in the Cochrane Library.

We explored the overall empirical distributions of the FI and FQ based on five common methods (Fisher's exact test, χ² test, risk difference, odds ratio, and relative risk) for determining statistical significance of binary outcomes in clinical research. We also considered three different scenarios for the FI calculation and evaluated the relationship between p values and FIs or FQs using Spearman's $$ . Finally, we summarized empirical thresholds based on the overall distributions of the FI and FQ to facilitate their interpretations in future research.

For about 20% of studies with significant results, the statistical significance was changed after modifying the event status of only one participant. Studies with significant results were considered slightly fragile if the significance hinged on the statuses of about five events. Studies were extremely fragile if FI $$ 1 or FQ $$ 0.01. The FIs were strongly correlated with p values for significant studies, while Spearman's $$ varied according to the total sample sizes of studies.

The statistical significance of clinical studies could be changed after modifying a few events' statuses. Many studies' findings are fairly fragile. The distributions of the FI and FQ provide insights for appraising the robustness of evidence in clinical decision-making.