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

BACKGROUND: 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...

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Autores principales: Bhattacharjee, Atanu, Vishwakarma, Gajendra K., Banerjee, Souvik, Shukla, Sharvari
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7422665/
https://www.ncbi.nlm.nih.gov/pubmed/32787822
http://dx.doi.org/10.1186/s12874-020-01090-z
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author Bhattacharjee, Atanu
Vishwakarma, Gajendra K.
Banerjee, Souvik
Shukla, Sharvari
author_facet Bhattacharjee, Atanu
Vishwakarma, Gajendra K.
Banerjee, Souvik
Shukla, Sharvari
author_sort Bhattacharjee, Atanu
collection PubMed
description BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
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spelling pubmed-74226652020-08-16 Disease progression of cancer patients during COVID-19 pandemic: a comprehensive analytical strategy by time-dependent modelling Bhattacharjee, Atanu Vishwakarma, Gajendra K. Banerjee, Souvik Shukla, Sharvari BMC Med Res Methodol Research Article BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. BioMed Central 2020-08-12 /pmc/articles/PMC7422665/ /pubmed/32787822 http://dx.doi.org/10.1186/s12874-020-01090-z Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Bhattacharjee, Atanu
Vishwakarma, Gajendra K.
Banerjee, Souvik
Shukla, Sharvari
Disease progression of cancer patients during COVID-19 pandemic: a comprehensive analytical strategy by time-dependent modelling
title Disease progression of cancer patients during COVID-19 pandemic: a comprehensive analytical strategy by time-dependent modelling
title_full Disease progression of cancer patients during COVID-19 pandemic: a comprehensive analytical strategy by time-dependent modelling
title_fullStr Disease progression of cancer patients during COVID-19 pandemic: a comprehensive analytical strategy by time-dependent modelling
title_full_unstemmed Disease progression of cancer patients during COVID-19 pandemic: a comprehensive analytical strategy by time-dependent modelling
title_short Disease progression of cancer patients during COVID-19 pandemic: a comprehensive analytical strategy by time-dependent modelling
title_sort disease progression of cancer patients during covid-19 pandemic: a comprehensive analytical strategy by time-dependent modelling
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7422665/
https://www.ncbi.nlm.nih.gov/pubmed/32787822
http://dx.doi.org/10.1186/s12874-020-01090-z
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