A method for calculating the fragility index of continuous outcomes


      • Fragility Index provides useful insight into how robust a statistically significant result really is.
      • Classic computations of Fragility Index are limited to dichotomous variables.
      • An iterative algorithm can be used to calculate Continuous Fragility Index (CFI) for continuous variables.
      • In settings where original data is not available, a multiple simulation technique can be used to estimate CFI.
      • The fragility of outcomes within the same study may vary considerably.



      Clinicians’ overdependence on p-values to determine significance in clinical trials is common yet potentially misleading. The Fragility Index (FI) describes how robust a significant result is by determining the number of events the statistical significance hinges on. However, this concept cannot be applied to nondichotomous variables. We describe a method to calculate a Continuous Fragility Index (CFI) for continuous variables. We further provide a method to estimate CFI when original data is not available.

      Study Design and Setting

      An iterative substitution algorithm is described to calculate CFI prospectively from data or retrospectively from summary statistics and its response to variations in the data is reported. We then apply this method to a previously published review as a proof-of-concept.


      The CFI increases linearly with sample size, logarithmically with mean difference, and decreases exponentially with standard deviation. Forty-eight studies were included of which 30 had significant non-dichotomous outcomes. CFI and FI were uncorrelated and mean CFI was significantly higher than FI (9 vs. 2, P< 0.001).


      Our algorithm extends fragility to continuous outcomes, expanding the applications of the fragility concept. The fragility of outcomes within a single study may vary based on variable type and should be evaluated independently.


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