How to Analyze Cancer Progression in COVID-19 Pandemic? [post]

Atanu Bhattacharjee, GajendraK.Vishwakarma, Souvik Banerjee, Sharvari Shukla
2020 unpublished
Background: The constant news about the coronavirus is scary. It is not possible to separate treatment for Cancer due to COVID-19. An effective treatment comparison strategy is needed. We need to have a handy tool to understand cancer progression in this unprecedented scenario. Linking different events of cancer progression is the need of the hour. It is a methodological challenge.Methods: This article is dedicated to explore the time lag effect and make statistical inference about the best
more » ... about the best experimental arm using Accelerated failure time model and regression methods. The work is presented as the occurrence of other events as a hazard rate after the first event (relapse). The time lag effect between the events is linked and analyzed. Results: The results are further analyzed and presented to show the efficacy of our study. An accelerated failure time model is also applied with the transition states and the dependency structure between the gap times are explained using auto-regression. The effects of Arms are compared using the coefficient of auto-regression and accelerated failure time (AFT) models.Conclusions: We provide the solutions to overcome the issue with interval between two consecutive events in motivating head and neck cancer (HNC) data. COVID-19 is not going to leave us soon. We have to carry several cancer clinical trials in presences of COVID-19. We presented a comprehensive analytical strategy to analyze cancer clinical trial data during this pandemic.Keywords: COVID-19, Accelerated Failure Time, Proportional Hazard Model, Bayesian, Auto-Regression.
doi:10.21203/rs.3.rs-31799/v1 fatcat:vc6j63nuqregpjwqebp5vvqmm4