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Addressing the Impact of the COVID-19 Pandemic on Survival Outcomes in Randomized Phase III Oncology Trials
Volume 21, Issue 4 (2023), pp. 781–798
Jiabu Ye †   Binbing Yu   Helen Mann     All authors (10)

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https://doi.org/10.6339/22-JDS1079
Pub. online: 7 December 2022      Type: Data Science In Action      Open accessOpen Access

† Former employee: research was conducted during the authors’ time at AstraZeneca.

Received
29 June 2022
Accepted
16 November 2022
Published
7 December 2022

Abstract

We assessed the impact of the coronavirus disease 2019 (COVID-19) pandemic on the statistical analysis of time-to-event outcomes in late-phase oncology trials. Using a simulated case study that mimics a Phase III ongoing trial during the pandemic, we evaluated the impact of COVID-19-related deaths, time off-treatment and missed clinical visits due to the pandemic, on overall survival and/or progression-free survival in terms of test size (also referred to as Type 1 error rate or alpha level), power, and hazard ratio (HR) estimates. We found that COVID-19-related deaths would impact both size and power, and lead to biased HR estimates; the impact would be more severe if there was an imbalance in COVID-19-related deaths between the study arms. Approaches censoring COVID-19-related deaths may mitigate the impact on power and HR estimation, especially if study data cut-off was extended to recover censoring-related event loss. The impact of COVID-19-related time off-treatment would be modest for power, and moderate for size and HR estimation. Different rules of censoring cancer progression times result in a slight difference in the power for the analysis of progression-free survival. The simulations provided valuable information for determining whether clinical-trial modifications should be required for ongoing trials during the COVID-19 pandemic.

Supplementary material

 Supplementary Material
More comprehensive description of simulation procedure and results are presented in the supplementary material. We also include the codes for simulations. The data underlying the findings described in this manuscript may be obtained in accordance with AstraZeneca’s data sharing policy described at: https://astrazenecagrouptrials.pharmacm.com/ST/Submission/Disclosure.

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Copyright
2023 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.
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Open access article under the CC BY license.

Keywords
model evaluation oncology optimal design protocol modification survival analysis trial simulation

Funding
This analysis was funded by AstraZeneca. Medical writing support, under the direction of the authors, was provided by Carole Mongin-Bulewski of Ashfield MedComms (Manchester, UK), an Inizio company, and was funded by AstraZeneca. AstraZeneca was involved in the study design; collection, analysis, and interpretation of data; writing of the manuscript; and decision to submit the article for publication.

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