Rights statement: The final, definitive version of this article has been published in the Journal, Clinical Trials, 17 (5), 2020, © SAGE Publications Ltd, 2020 by SAGE Publications Ltd at the Clinical Trials page: https://journals.sagepub.com/home/ctj http://journals.sagepub.com/
Accepted author manuscript, 1.31 MB, PDF document
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Endpoints for randomized controlled clinical trials for COVID-19 treatments
AU - Dodd, L.E.
AU - Follmann, D.
AU - Wang, J.
AU - Koenig, F.
AU - Korn, L.L.
AU - Schoergenhofer, C.
AU - Proschan, M.
AU - Hunsberger, S.
AU - Bonnett, T.
AU - Makowski, M.
AU - Belhadi, D.
AU - Wang, Y.
AU - Cao, B.
AU - Mentre, F.
AU - Jaki, T.
N1 - The final, definitive version of this article has been published in the Journal, Clinical Trials, 17 (5), 2020, © SAGE Publications Ltd, 2020 by SAGE Publications Ltd at the Clinical Trials page: https://journals.sagepub.com/home/ctj http://journals.sagepub.com/
PY - 2020/10/1
Y1 - 2020/10/1
N2 - Background: Endpoint choice for randomized controlled trials of treatments for novel coronavirus-induced disease (COVID-19) is complex. Trials must start rapidly to identify treatments that can be used as part of the outbreak response, in the midst of considerable uncertainty and limited information. COVID-19 presentation is heterogeneous, ranging from mild disease that improves within days to critical disease that can last weeks to over a month and can end in death. While improvement in mortality would provide unquestionable evidence about the clinical significance of a treatment, sample sizes for a study evaluating mortality are large and may be impractical, particularly given a multitude of putative therapies to evaluate. Furthermore, patient states in between “cure” and “death” represent meaningful distinctions. Clinical severity scores have been proposed as an alternative. However, the appropriate summary measure for severity scores has been the subject of debate, particularly given the variable time course of COVID-19. Outcomes measured at fixed time points, such as a comparison of severity scores between treatment and control at day 14, may risk missing the time of clinical benefit. An endpoint such as time to improvement (or recovery) avoids the timing problem. However, some have argued that power losses will result from reducing the ordinal scale to a binary state of “recovered” versus “not recovered.” Methods: We evaluate statistical power for possible trial endpoints for COVID-19 treatment trials using simulation models and data from two recent COVID-19 treatment trials. Results: Power for fixed time-point methods depends heavily on the time selected for evaluation. Time-to-event approaches have reasonable statistical power, even when compared with a fixed time-point method evaluated at the optimal time. Discussion: Time-to-event analysis methods have advantages in the COVID-19 setting, unless the optimal time for evaluating treatment effect is known in advance. Even when the optimal time is known, a time-to-event approach may increase power for interim analyses. © The Author(s) 2020.
AB - Background: Endpoint choice for randomized controlled trials of treatments for novel coronavirus-induced disease (COVID-19) is complex. Trials must start rapidly to identify treatments that can be used as part of the outbreak response, in the midst of considerable uncertainty and limited information. COVID-19 presentation is heterogeneous, ranging from mild disease that improves within days to critical disease that can last weeks to over a month and can end in death. While improvement in mortality would provide unquestionable evidence about the clinical significance of a treatment, sample sizes for a study evaluating mortality are large and may be impractical, particularly given a multitude of putative therapies to evaluate. Furthermore, patient states in between “cure” and “death” represent meaningful distinctions. Clinical severity scores have been proposed as an alternative. However, the appropriate summary measure for severity scores has been the subject of debate, particularly given the variable time course of COVID-19. Outcomes measured at fixed time points, such as a comparison of severity scores between treatment and control at day 14, may risk missing the time of clinical benefit. An endpoint such as time to improvement (or recovery) avoids the timing problem. However, some have argued that power losses will result from reducing the ordinal scale to a binary state of “recovered” versus “not recovered.” Methods: We evaluate statistical power for possible trial endpoints for COVID-19 treatment trials using simulation models and data from two recent COVID-19 treatment trials. Results: Power for fixed time-point methods depends heavily on the time selected for evaluation. Time-to-event approaches have reasonable statistical power, even when compared with a fixed time-point method evaluated at the optimal time. Discussion: Time-to-event analysis methods have advantages in the COVID-19 setting, unless the optimal time for evaluating treatment effect is known in advance. Even when the optimal time is known, a time-to-event approach may increase power for interim analyses. © The Author(s) 2020.
KW - censoring
KW - clinical trials
KW - COVID-19
KW - endpoints
KW - log-rank test
KW - proportional odds model
KW - WHO ordinal scale
U2 - 10.1177/1740774520939938
DO - 10.1177/1740774520939938
M3 - Journal article
VL - 17
SP - 472
EP - 482
JO - Clinical Trials
JF - Clinical Trials
SN - 1740-7745
IS - 5
ER -