Fragility Index for Time-to-Event Endpoints in Single-Arm Clinical Trials

TL;DR

This paper introduces the Fragility Index for time-to-event endpoints in single-arm clinical trials, providing a new metric to assess the robustness of study conclusions using a Bayesian exponential survival model.

Contribution

It presents a novel Fragility Index for survival data in single-arm trials and offers a practical R package for its computation, enhancing robustness assessment.

Findings

FI quantifies robustness of median survival conclusions.

Application to real trials demonstrates FI's utility.

FI helps identify fragile study results.

Abstract

The reliability of clinical trial outcomes is crucial, especially in guiding medical decisions. In this paper, we introduce the Fragility Index (FI) for time-to-event endpoints in single-arm clinical trials - a novel metric designed to quantify the robustness of study conclusions. The FI represents the smallest number of censored observations that, when reclassified as uncensored events, causes the posterior probability of the median survival time exceeding a specified threshold to fall below a predefined confidence level. While drug effectiveness is typically assessed by determining whether the posterior probability exceeds a specified confidence level, the FI offers a complementary measure, indicating how robust these conclusions are to potential shifts in the data. Using a Bayesian approach, we develop a practical framework for computing the FI based on the exponential survival model. To facilitate the application of our method, we developed an R package fi, which provides a tool to compute the Fragility Index. Through real world case studies involving time to event data from single arms clinical trials, we demonstrate the utility of this index. Our findings highlight how the FI can be a valuable tool for assessing the robustness of survival analyses in single-arm studies, aiding researchers and clinicians in making more informed decisions.