Disease progression of cancer patients during COVID-19 pandemic: a comprehensive analytical strategy by time-dependent modelling

• Published in: BMC medical research methodology 2020-08, Vol.20 (1), p.209-209, Article 209
• Main Authors: Bhattacharjee, Atanu, Vishwakarma, Gajendra K, Banerjee, Souvik, Shukla, Sharvari
• Format: Article
• Language: English
• Subjects: Accelerated failure time; Algorithms; Auto-regression; Bayes Theorem; Bayesian; Betacoronavirus - physiology; Cancer; Care and treatment; Clinical trials; Coronavirus Infections - complications; Coronavirus Infections - prevention & control; Coronavirus Infections - virology; Coronaviruses; COVID-19; Data analysis; Development and progression; Disease Progression; Epidemics; Head & neck cancer; Head and Neck Neoplasms - complications; Head and Neck Neoplasms - diagnosis; Head and Neck Neoplasms - therapy; Humans; Infections; Kaplan-Meier Estimate; Markov Chains; Medical Oncology - methods; Medical research; Models, Theoretical; Monte Carlo Method; Neoplasm Recurrence, Local; Pandemics - prevention & control; Patients; Pneumonia, Viral - complications; Pneumonia, Viral - prevention & control; Pneumonia, Viral - virology; Proportional Hazard model; Random variables; Regression analysis; Research methodology; SARS-CoV-2; Statistics
• ISSN: 1471-2288; 1471-2288
• DOI: 10.1186/s12874-020-01090-z

As the whole world is experiencing the cascading effect of a new pandemic, almost every aspect of modern life has been disrupted. Because of health emergencies during this period, widespread fear has resulted in compromised patient safety, especially for patients with cancer. It is very challenging to treat such cancer patients because of the complexity of providing care and treatment, along with COVID-19. Hence, 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 huge challenge for the development of new methodology. This article explores the time lag effect and makes a statistical inference about the best experimental arm using Accelerated Failure Time (AFT) 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 analysed. The results were presented as a comprehensive analytical strategy by joining all disease progression. An AFT model applied with the transition states, and the dependency structure between the gap times was used by the auto-regression model. The effects of arms were compared using the coefficient of auto-regression and accelerated failure time (AFT) models. We provide the solutions to overcome the issue with intervals between two consecutive events in motivating head and neck cancer (HNC) data. COVID-19 is not going to leave us soon. We have to conduct several cancer clinical trials in the presence of COVID-19. A comprehensive analytical strategy to analyse cancer clinical trial data during COVID-19 pandemic is presented.