Jon-Michael E Caldwell1, Keon Youssefzadeh2, Orr Limpisvasti2. 1. Cedars-Sinai Kerlan-Jobe Orthopedic Institute, Los Angeles, CA. Electronic address: caldwellorthopedics@gmail.com. 2. Cedars-Sinai Kerlan-Jobe Orthopedic Institute, Los Angeles, CA.
Abstract
OBJECTIVE: 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. RESULTS: 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). CONCLUSION: 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.
OBJECTIVE: 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. RESULTS: 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). CONCLUSION: 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.
Authors: Benjamin R Baer; Mario Gaudino; Mary Charlson; Stephen E Fremes; Martin T Wells Journal: Proc Natl Acad Sci U S A Date: 2021-12-07 Impact factor: 12.779
Authors: Nathan P Fackler; Theofilos Karasavvidis; Cooper B Ehlers; Kylie T Callan; Wilson C Lai; Robert L Parisien; Dean Wang Journal: Foot Ankle Int Date: 2022-08-24 Impact factor: 3.569