Benjamin R Baer1, Mario Gaudino2, Stephen E Fremes3, Mary Charlson4, Martin T Wells5. 1. Department of Statistics and Data Science, Cornell University, Ithaca, NY, USA. Electronic address: brb225@cornell.edu. 2. Department of Cardiothoracic Surgery, Weill Cornell Medicine, New York, NY, USA. 3. Schulich Heart Centre, Sunnybrook Health Science, University of Toronto, Toronto, Ontario, Canada. 4. Department of Medicine, Weill Cornell Medicine, New York, NY, USA. 5. Department of Statistics and Data Science, Cornell University, Ithaca, NY, USA; Department of Medicine, Weill Cornell Medicine, New York, NY, USA.
Abstract
OBJECTIVE: The fragility index is a clinically interpretable metric increasingly used to interpret the robustness of clinical trials results that is generally not incorporated in sample size calculation and applied post-hoc. In this manuscript, we propose to base the sample size calculation on the fragility index in a way that supplements the classical prefixed alpha and power cutoffs and we provide a dedicated R software package for the design and analysis tools. STUDY DESIGN AND SETTING: This approach follows from a novel hypothesis testing framework that is based on the fragility index and builds on the classical testing approach. As case studies, we re-analyse the design of two important trials in cardiovascular medicine, the FAME and FAMOUS-NSTEMI trials. RESULTS: The analyses show that approach returns sample sizes which results in a higher power for the P value based test and most importantly a lower and context dependent Type I error rate for the fragility index based test compared to standard tests. CONCLUSION: Our method allows clinicians to control for the fragility index during clinical trial design.
OBJECTIVE: The fragility index is a clinically interpretable metric increasingly used to interpret the robustness of clinical trials results that is generally not incorporated in sample size calculation and applied post-hoc. In this manuscript, we propose to base the sample size calculation on the fragility index in a way that supplements the classical prefixed alpha and power cutoffs and we provide a dedicated R software package for the design and analysis tools. STUDY DESIGN AND SETTING: This approach follows from a novel hypothesis testing framework that is based on the fragility index and builds on the classical testing approach. As case studies, we re-analyse the design of two important trials in cardiovascular medicine, the FAME and FAMOUS-NSTEMI trials. RESULTS: The analyses show that approach returns sample sizes which results in a higher power for the P value based test and most importantly a lower and context dependent Type I error rate for the fragility index based test compared to standard tests. CONCLUSION: Our method allows clinicians to control for the fragility index during clinical trial design.
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