Meaningful Change Index: A P-Value Independent Metric for Assessing Robustness and Fragility in Continuous Outcomes

The assessment of statistical robustness in clinical trials with continuous outcomes has relied heavily on p-value dependent metrics that fail to address clinically meaningful differences. This study introduces the Meaningful Change Index - MeCI - a novel metric applied to continuous data that quantifies how distinguishable treatment groups are based on the crossover point of their sample distributions. It is independent of statistical significance testing. The Meaningful Change Index is defined as the minimum distance from group means to their distributional crossover point, normalized by the combined standard deviations. Unlike existing continuous fragility indices that depend on p-value thresholds, Meaningful Change Index focuses on a statistically meaningful numeric separation between populations. A worked example demonstrates temporal patterns of treatment effect evolution, revealing how group distinguishability changes over time despite robust binary outcomes when arbitrary thresholds are applied to the data. Meaningful Change Index provides clinically relevant fragility and robustness assessment for continuous data without dependence on statistical significance conventions, offering superior insights into treatment effect sustainability and group separation. Version 2: Terminology updated from 'Meaningful Change Fragility (MCF)' to 'Meaningful Change Index (MeCI)' (MEK-see-eye) for clarity and collision avoidance. Core methodology unchanged.