How to Quantify and Interpret Treatment Effects in Comparative Clinical Studies of COVID-19
Authors: Michael A. Proschan, Dean Follmann, Lori Dodd
Journal: Annals of Internal Medicine (2021)

LettersMay 2021How to Quantify and Interpret Treatment Effects in Comparative Clinical Studies of COVID-19FREEMichael A. Proschan, PhD, Dean Follmann, PhD, Lori Dodd, PhDMichael A. Proschan, PhDNational Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, Dean Follmann, PhDNational Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, Lori Dodd, PhDNational Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MarylandAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/L20-1440 SectionsAboutVisual AbstractPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail TO THE EDITOR: McCaw and colleagues (1) raised issues about recovery in the presence of the competing risk for death in the context of COVID-19 trials. We contend that our setting slightly differs from the typical competing risk setting. A typical competing risk scenario might involve time to coronary heart disease death, with the patient dying of stroke. Having a stroke is undoubtedly informative about what that patient's time to coronary heart disease death would have been, but we cannot reliably impute the time to coronary heart disease death. In the COVID-19 setting, the patient is dying of COVID-19 itself.Death gives complete information about time to recovery; it is infinite. We therefore respectfully disagree with McCaw and colleagues' statement, “In fact, the recovery times of patients who have died could not be defined or estimated.” Survival software does not allow infinite times to recovery, but imputing any time longer than the longest possible follow-up (28 days in the ACTT-1 [Adaptive COVID-19 Treatment Trial]) fortunately gives the same answer. We also disagree with the statement, “The strategy adopted by ACTT-1 investigators for managing death as a competing risk is unusual.” In fact, it is common in clinical trials to assign the worst possible outcome to a patient who dies of the disease under study. In addition, with complete follow-up, our approach corresponds to the widely used Fine–Gray method for competing risks and our Kaplan–Meier curve corresponds to the cumulative incidence curve.We acknowledge that our method poses an issue at an interim analysis. A patient enrolled 15 days ago who dies on study day 7 has an imputed 29 days of follow-up. The cumulative incidence and Fine–Gray approaches censor such a patient according to the eligible follow-up window, which for this patient would be 15 days. In the interim report of ACTT-1, the 2 approaches gave virtually identical answers; yet, we acknowledge that the Fine–Gray and cumulative incidence approaches are theoretically superior for interim analyses.We all agree on certain statements, such as, “Moreover, because the potential death and recovery times of each patient are probably correlated, standard survival analysis methods that treat death as independent of censoring are not appropriate.” A standard survival analysis would censor recovery time at the time of death, which makes no sense when censoring is informative. The Kaplan–Meier method does make sense when imputing a recovery time that exceeds the longest possible follow-up. We also appreciate the interpretation of a restricted mean time as the expected time in recovery. We believe that it is a useful perspective. However, we believe that the Kaplan–Meier method in conjunction with imputation of the longest recovery time is also useful.Reference1. McCaw ZR, Tian L, Vassy JL, et al. How to quantify and interpret treatment effects in comparative clinical studies of COVID-19. Ann Intern Med. 2020;173:632-7. [PMID: 32634024]. doi:10.7326/M20-4044 LinkGoogle Scholar Comments 0 Comments Sign In to Submit A Comment Author, Article, and Disclosure InformationAuthors: Michael A. Proschan, PhD; Dean Follmann, PhD; Lori Dodd, PhDAffiliations: National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MarylandDisclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=L20-1440. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoHow to Quantify and Interpret Treatment Effects in Comparative Clinical Studies of COVID-19 Zachary R. McCaw , Lu Tian , Jason L. Vassy , Christine Seel Ritchie , Chien-Chang Lee , Dae Hyun Kim , and Lee-Jen Wei How to Quantify and Interpret Treatment Effects in Comparative Clinical Studies of COVID-19 Lu Tian , Jason L. Vassy , Christine Seel Ritchie , Chien-Chang Lee , Dae Hyun Kim How to Quantify and Interpret Treatment Effects in Comparative Clinical Studies of COVID-19 Derek Hazard , Klaus Kaier , Maja von Cube , Jerome Lambert , Martin Wolkewitz Metrics May 2021Volume 174, Issue 5 Page: 730-731 ePublished: 18 May 2021 Issue Published: May 2021 Copyright & PermissionsCopyright © 2021 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...