1766P COVID-19 and lung cancer: What do we know?

Serrano, Gabriela; Rogado, Jacobo; Pangua, Cristina; Obispo, Berta; Marino, A. Martin; Pérez-Pérez, M.; López-Alfonso, Ana; Lara, M.A.

doi:10.1016/j.annonc.2020.08.1830

Cited by 1 publication

(2 citation statements)

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“…As a pandemic may cause patients to miss on-study procedures or clinical visits (Serrano et al, 2020;Pinato et al, 2020;Seguí et al, 2020), we assessed the impact of time-off-treatment on HR estimates, type I error, and power. The simulations of the impact of time-off-treatment due to COVID-19 showed a modest impact on power for both PFS and OS, and a moderate impact on type I error and HR estimates.…”

Section: Discussionmentioning

confidence: 99%

“…Early evidence also suggests that COVID-19 prevalence is disproportionately higher in cancer patients (particularly in those with lung cancer) compared with the general population (Bakouny et al, 2020(Bakouny et al, , 2022Sessa et al, 2022). Notably, however, cancer treatment itself has been shown not to be a risk factor for COVID-19-related mortality (Serrano et al, 2020;Pinato et al, 2020;Seguí et al, 2020;Tapia et al, 2020;García-Illescas et al, 2020;Joerger et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Addressing the Impact of the COVID-19 Pandemic on Survival Outcomes in Randomized Phase III Oncology Trials

Ye¹,

Yu²,

Mann³

et al. 2022

Journal of Data Science

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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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Section: Discussionmentioning

confidence: 99%