Factors that impact fragility index and their visualizations.

Abstract

As the recent literature has growing concerns about research replicability and the misuse and misconception of P-values, the fragility index (FI) has been an attractive measure to assess the robustness (or fragility) of clinical study results with binary outcomes. It is defined as the minimum number of event status modifications that can alter a study result's statistical significance (or non-significance). Owing to its intuitive concept, the FI has been applied to assess the fragility of clinical studies of various specialties. However, the FI may be limited in certain settings. As a relatively new measure, more work is needed to examine its properties. This article explores several factors that may impact the derivation of the FI, including how event status is modified and the impact of significance levels. Moreover, we propose novel methods to visualize the fragility of a study's result. These factors and methods are illustrated using worked examples of artificial datasets. Randomized controlled trials on antidepressant drugs are also used to evaluate their real-world performance. The FI depends on the treatment arm(s) in which event status is modified, whether the original study result is significant, the statistical method used for calculating the P-value, and the threshold for determining statistical significance. Also, the proposed visualization methods can clearly demonstrate a study result's fragility, which may be useful supplements to the single value of the FI. Our findings may help clinicians properly use the FI and appraise the reliability of a study's conclusion.