The fragility index can be used for sample size calculations in clinical trials

被引:29
作者
Baer, Benjamin R. [1 ]
Gaudino, Mario [2 ]
Fremes, Stephen E. [3 ]
Charlson, Mary [4 ]
Wells, Martin T. [1 ,4 ]
机构
[1] Cornell Univ, Dept Stat & Data Sci, Ithaca, NY 14850 USA
[2] Weill Cornell Med, Dept Cardiothorac Surg, New York, NY USA
[3] Univ Toronto, Sunnybrook Hlth Sci, Schulich Heart Ctr, Toronto, ON, Canada
[4] Weill Cornell Med, Dept Med, New York, NY USA
关键词
Fragility index; P value; Statistical significance; Research methods; Sample size calculation; Trial design; STATISTICALLY SIGNIFICANT FINDINGS; RANDOMIZED-TRIALS; P-VALUE; SURGERY;
D O I
10.1016/j.jclinepi.2021.08.010
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
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. (C) 2021 Elsevier Inc. All rights reserved.
引用
收藏
页码:199 / 209
页数:11
相关论文
共 34 条
[21]   The testing of statistical hypotheses in relation to probabilities a priori. [J].
Neyman, J ;
Pearson, ES .
PROCEEDINGS OF THE CAMBRIDGE PHILOSOPHICAL SOCIETY, 1933, 29 :492-510
[22]  
Neyman J, 1928, BIOMETRIKA, V20A, P175, DOI 10.2307/2331945
[23]   ACCELERATION OF STOCHASTIC-APPROXIMATION BY AVERAGING [J].
POLYAK, BT ;
JUDITSKY, AB .
SIAM JOURNAL ON CONTROL AND OPTIMIZATION, 1992, 30 (04) :838-855
[24]   Dismantling the Fragility Index: A demonstration of statistical reasoning [J].
Potter, Gail E. .
STATISTICS IN MEDICINE, 2020, 39 (26) :3720-3731
[25]  
R Core Team, 2020, R: A Language and Environment for Statistical Computing, DOI DOI 10.1128/EC.4.8.1455-1464.2005
[26]   The Fragility Index in Multicenter Randomized Controlled Critical Care Trials [J].
Ridgeon, Elliott E. ;
Young, Paul J. ;
Bellomo, Rinaldo ;
Mucchetti, Marta ;
Lembo, Rosalba ;
Landoni, Giovanni .
CRITICAL CARE MEDICINE, 2016, 44 (07) :1278-1284
[27]   The fragility of significant results underscores the need of larger randomized controlled trials in nephrology [J].
Shochet, Lani R. ;
Kerr, Peter G. ;
Polkinghorne, Kevan R. .
KIDNEY INTERNATIONAL, 2017, 92 (06) :1469-1475
[28]   p-Curve and Effect Size: Correcting for Publication Bias Using Only Significant Results [J].
Simonsohn, Uri ;
Nelson, Leif D. ;
Simmons, Joseph P. .
PERSPECTIVES ON PSYCHOLOGICAL SCIENCE, 2014, 9 (06) :666-681
[29]   P-Curve: A Key to the File-Drawer [J].
Simonsohn, Uri ;
Nelson, Leif D. ;
Simmons, Joseph P. .
JOURNAL OF EXPERIMENTAL PSYCHOLOGY-GENERAL, 2014, 143 (02) :534-547
[30]   The Fragility Index in Randomized Clinical Trials as a Means of Optimizing Patient Care [J].
Tignanelli, Christopher J. ;
Napolitano, Lena M. .
JAMA SURGERY, 2019, 154 (01) :74-79