Fragility Measures For Typical Cases

TL;DR

This paper introduces stochastic generalized fragility indices to address limitations in the traditional fragility index, providing more reliable case selection for hypothesis testing in clinical and causal studies.

Contribution

It presents a new method, the stochastic generalized fragility indices, to improve case selection in fragility measures, illustrated with electoral and smoking cessation examples.

Findings

The new method remedies the case selection drawback of traditional fragility indices.

Illustrations demonstrate the method's application to electoral outcomes.

The approach enhances the interpretability of fragility measures in hypothesis testing.

Abstract

The fragility index is a clinically motivated metric designed to supplement the $p$ value during hypothesis testing. The measure relies on two pillars: selecting cases to have their outcome modified and modifying the outcomes. The measure is interesting but the case selection suffers from a drawback which can hamper its interpretation. This work presents the drawback and a method, the stochastic generalized fragility indices, designed to remedy it. Two examples concerning electoral outcomes and the causal effect of smoking cessation illustrate the method.