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Handling death as an intercurrent event in time to recovery analysis in COVID-19 treatment clinical trials.
Li, Hong; Gleason, Kevin J; Hu, Yiran; Lovell, Sandra S; Mukhopadhyay, Saurabh; Wang, Li; Huang, Bidan.
  • Li H; Data and Statistical Sciences, AbbVie, North Chicago, USA. Electronic address: lena.lee.76@gmail.com.
  • Gleason KJ; Data and Statistical Sciences, AbbVie, North Chicago, USA.
  • Hu Y; Data and Statistical Sciences, AbbVie, North Chicago, USA.
  • Lovell SS; Data and Statistical Sciences, AbbVie, North Chicago, USA.
  • Mukhopadhyay S; Data and Statistical Sciences, AbbVie, North Chicago, USA.
  • Wang L; Data and Statistical Sciences, AbbVie, North Chicago, USA.
  • Huang B; Data and Statistical Sciences, AbbVie, North Chicago, USA.
Contemp Clin Trials ; 119: 106758, 2022 08.
Article in English | MEDLINE | ID: covidwho-1773152
ABSTRACT
In clinical trials with the objective to evaluate the treatment effect on time to recovery, such as investigational trials on therapies for COVID-19 hospitalized patients, the patients may face a mortality risk that competes with the opportunity to recover (e.g., be discharged from the hospital). Therefore, an appropriate analytical strategy to account for death is particularly important due to its potential impact on the estimation of the treatment effect. To address this challenge, we conducted a thorough evaluation and comparison of nine survival analysis methods with different strategies to account for death, including standard survival analysis methods with different censoring strategies and competing risk analysis methods. We report results of a comprehensive simulation study that employed design parameters commonly seen in COVID-19 trials and case studies using reconstructed data from a published COVID-19 clinical trial. Our research results demonstrate that, when there is a moderate to large proportion of patients who died before observing their recovery, competing risk analyses and survival analyses with the strategy to censor death at the maximum follow-up timepoint would be able to better detect a treatment effect on recovery than the standard survival analysis that treat death as a non-informative censoring event. The aim of this research is to raise awareness of the importance of handling death appropriately in the time-to-recovery analysis when planning current and future COVID-19 treatment trials.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Death / COVID-19 Drug Treatment Type of study: Cohort study / Experimental Studies / Prognostic study / Randomized controlled trials Limits: Humans Language: English Journal: Contemp Clin Trials Journal subject: Medicine / Therapeutics Year: 2022 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Death / COVID-19 Drug Treatment Type of study: Cohort study / Experimental Studies / Prognostic study / Randomized controlled trials Limits: Humans Language: English Journal: Contemp Clin Trials Journal subject: Medicine / Therapeutics Year: 2022 Document Type: Article