Michael W Sjoding1,2, David A Schoenfeld3, Samuel M Brown4, Catherine L Hough5, Donald M Yealy6, Marc Moss7, Derek C Angus8, Theodore J Iwashyna1,9. 1. 1 Department of Internal Medicine and. 2. 2 Institute for Healthcare Policy & Innovation, University of Michigan, Ann Arbor, Michigan. 3. 3 Biostatistics Unit, Massachusetts General Hospital and Harvard University, Boston, Massachusetts. 4. 4 Department of Pulmonary and Critical Care Medicine, Intermountain Medical Center and University of Utah School of Medicine, Salt Lake City, Utah. 5. 5 Division of Pulmonary and Critical Care Medicine, Harborview Medical Center, University of Washington, Seattle, Washington. 6. 6 Department of Emergency Medicine and. 7. 7 Department of Medicine, University of Colorado, Denver, Colorado; and. 8. 8 Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania. 9. 9 Veterans Affairs Center for Clinical Management Research, Ann Arbor, Michigan.
Abstract
RATIONALE: After the sample size of a randomized clinical trial (RCT) is set by the power requirement of its primary endpoint, investigators select secondary endpoints while unable to further adjust sample size. How the sensitivity and specificity of an instrument used to measure these outcomes, together with their expected underlying event rates, affect an RCT's power to measure significant differences in these outcomes is poorly understood. OBJECTIVES: Motivated by the design of an RCT of neuromuscular blockade in acute respiratory distress syndrome, we examined how power to detect a difference in secondary endpoints varies with the sensitivity and specificity of the instrument used to measure such outcomes. METHODS: We derived a general formula and Stata code for calculating an RCT's power to detect differences in binary outcomes when such outcomes are measured with imperfect sensitivity and specificity. The formula informed the choice of instrument for measuring post-traumatic stress-like symptoms in the Reevaluation of Systemic Early Neuromuscular Blockade RCT ( www.clinicaltrials.gov identifier NCT02509078). MEASUREMENTS AND MAIN RESULTS: On the basis of published sensitivities and specificities, the Impact of Events Scale-Revised was predicted to measure a 36% symptom rate, whereas the Post-Traumatic Stress Symptoms instrument was predicted to measure a 23% rate, if the true underlying rate of post-traumatic stress symptoms were 25%. Despite its lower sensitivity, the briefer Post-Traumatic Stress Symptoms instrument provided superior power to detect a difference in rates between trial arms, owing to its higher specificity. CONCLUSIONS: Examining instruments' power to detect differences in outcomes may guide their selection when multiple instruments exist, each with different sensitivities and specificities.
RCT Entities:
RATIONALE: After the sample size of a randomized clinical trial (RCT) is set by the power requirement of its primary endpoint, investigators select secondary endpoints while unable to further adjust sample size. How the sensitivity and specificity of an instrument used to measure these outcomes, together with their expected underlying event rates, affect an RCT's power to measure significant differences in these outcomes is poorly understood. OBJECTIVES: Motivated by the design of an RCT of neuromuscular blockade in acute respiratory distress syndrome, we examined how power to detect a difference in secondary endpoints varies with the sensitivity and specificity of the instrument used to measure such outcomes. METHODS: We derived a general formula and Stata code for calculating an RCT's power to detect differences in binary outcomes when such outcomes are measured with imperfect sensitivity and specificity. The formula informed the choice of instrument for measuring post-traumatic stress-like symptoms in the Reevaluation of Systemic Early Neuromuscular Blockade RCT ( www.clinicaltrials.gov identifier NCT02509078). MEASUREMENTS AND MAIN RESULTS: On the basis of published sensitivities and specificities, the Impact of Events Scale-Revised was predicted to measure a 36% symptom rate, whereas the Post-Traumatic Stress Symptoms instrument was predicted to measure a 23% rate, if the true underlying rate of post-traumatic stress symptoms were 25%. Despite its lower sensitivity, the briefer Post-Traumatic Stress Symptoms instrument provided superior power to detect a difference in rates between trial arms, owing to its higher specificity. CONCLUSIONS: Examining instruments' power to detect differences in outcomes may guide their selection when multiple instruments exist, each with different sensitivities and specificities.
Entities:
Keywords:
bias; clinical trials; critical care outcomes; sensitivity; specificity
Authors: Theodore J Iwashyna; James F Burke; Jeremy B Sussman; Hallie C Prescott; Rodney A Hayward; Derek C Angus Journal: Am J Respir Crit Care Med Date: 2015-11-01 Impact factor: 21.405
Authors: Kitty S Chan; Elizabeth R Pfoh; Linda Denehy; Doug Elliott; Anne E Holland; Victor D Dinglas; Dale M Needham Journal: Chest Date: 2015-05 Impact factor: 9.410
Authors: C Stoll; H P Kapfhammer; H B Rothenhäusler; M Haller; J Briegel; M Schmidt; T Krauseneck; K Durst; G Schelling Journal: Intensive Care Med Date: 1999-07 Impact factor: 17.440
Authors: Ann M Parker; Thiti Sricharoenchai; Sandeep Raparla; Kyle W Schneck; O Joseph Bienvenu; Dale M Needham Journal: Crit Care Med Date: 2015-05 Impact factor: 7.598
Authors: James C Jackson; Pratik P Pandharipande; Timothy D Girard; Nathan E Brummel; Jennifer L Thompson; Christopher G Hughes; Brenda T Pun; Eduard E Vasilevskis; Alessandro Morandi; Ayumi K Shintani; Ramona O Hopkins; Gordon R Bernard; Robert S Dittus; E Wesley Ely Journal: Lancet Respir Med Date: 2014-04-07 Impact factor: 30.700