JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1079 10.6339/22-JDS1079 Data Science in Action Addressing the Impact of the COVID-19 Pandemic on Survival Outcomes in Randomized Phase III Oncology Trials YeJiabu1† YuBinbingBinbing.Yu@astrazeneca.com1∗ MannHelen2 SabinAntony2† SzijgyartoZsolt2† WrightDavid3 MukhopadhyayPralay1† MassacesiCristian4 GhiorghiuSerban4 IaconaRenee1 Oncology Biometrics, Oncology R&D, AstraZeneca, Gaithersburg, Maryland, USA Oncology Biometrics, Oncology R&D, AstraZeneca, Cambridge, United Kingdom Early Biometrics & Statistical Innovation, Biopharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom Oncology R&D, AstraZeneca, Gaithersburg, Maryland, USA Corresponding author. Email: Binbing.Yu@astrazeneca.com.

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

 20237122022214781798 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.

296202216112022 2023 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2023 Open access article under the CC BY license.

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.

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