OncCOVID: Integrated Survival Estimates from Cancer Treatment Delay During the Covid-19 Pandemic

• Activity Number: 194 - Topics in Clinical Trials - I
• Type: Contributed
• Date/Time: Tuesday, August 4, 2020 : 10:00 AM to 2:00 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Holly Hartman* and Kelley Kidwell and Matthew Schipper
• Companies: University of Michigan and University of Michigan and University of Michigan
• Keywords: COVID-19; oncology; survival; personalized medicine; big data; bayesian

Abstract

Cancer is a leading cause of mortality, and treatment delay variably impacts cancer-specific survival. Treatment delay is occurring during the SARS-COV 2 pandemic, but may reduce COVID-19-specific mortality in this at risk population. To optimize resource allocation, quantitative integration of cancer- and Covid-19-specific mortality estimates are needed. We developed a model called OncCOVID to calculate the overall survival and restricted mean survival time estimates for cancer patients under both immediate treatment and delaying treatment. We combine data from published literature and open source data sets to estimate the effects of COVID-19. We also use large population based datasets for estimating the overall survival of the patient pre-COVID-19 and the effects of delaying treatment. All estimates are based on patient demographics, including location, and thus the OncCOVID model results in personalized estimates for the effect of delaying treatment. The OncCOVID web-based application (http://onccovid.med.umich.edu/) can quantitatively aid in individualized cancer treatment resource allocation during the Covid-19 global pandemic.