Joint modeling of longitudinal change in tumor cell level and time to death of breast cancer patients: In case of Ayder comprehensive specialized Hospital Tigray, Ethiopia

Abstract: Breast cancer is the major public health problem throughout the world and it results in serious physical damages and death. This work proposes the use of joint model to study breast cancer in patients of Ayder Hospital. The primary motivation is to contribute to the understanding of the tu...

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Main Authors: Bsrat Tesfay, Tewodros Getinet, Endeshaw Assefa Derso
Format: Article
Language:English
Published: Taylor & Francis Group 2021-01-01
Series:Cogent Medicine
Subjects:
Online Access:http://dx.doi.org/10.1080/2331205X.2021.1874090
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author Bsrat Tesfay
Tewodros Getinet
Endeshaw Assefa Derso
author_facet Bsrat Tesfay
Tewodros Getinet
Endeshaw Assefa Derso
author_sort Bsrat Tesfay
collection DOAJ
description Abstract: Breast cancer is the major public health problem throughout the world and it results in serious physical damages and death. This work proposes the use of joint model to study breast cancer in patients of Ayder Hospital. The primary motivation is to contribute to the understanding of the tumor cell progression of breast cancer, within Ayder Hospital, using a joint model that takes into account a possible existence of a serial correlation structure within a same subject observations from September 2015 till December 2018. The general aim of this study was to investigate the risk of longitudinal change in tumor cell level on time to death due to breast cancer among breast cancer patients. Hospital-based retrospective cohort study was conducted among breast cancer patients. A joint model of longitudinal and time to death model was used to determine the risk of longitudinal change in tumor cell level on time to death due to breast cancer patients. These were used by using JM package in R version. Results from joint models, showed that the longitudinal Tumor cell progression was signicantly associated with the survival probability of these patients(estimated association parameter(ɑ) in the joint model is 0.84 with corresponding (95% CI: 2.28,2.37). A comparison between parameter estimates obtained in this joint model and independent survival and longitudinal analysis lead us to conclude that independent analysis brings up bias parameter estimates. There is a strong association between the progression change in log(TCL) and risk of mortality due to breast cancer.
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spelling doaj.art-28ca1dabcc1647348938ddc046f27d1e2022-12-22T03:37:58ZengTaylor & Francis GroupCogent Medicine2331-205X2021-01-018110.1080/2331205X.2021.18740901874090Joint modeling of longitudinal change in tumor cell level and time to death of breast cancer patients: In case of Ayder comprehensive specialized Hospital Tigray, EthiopiaBsrat Tesfay0Tewodros Getinet1Endeshaw Assefa Derso2Mekelle UniversitySt. Paul’s Hospital Millennium Medical CollegeUniversity of GondarAbstract: Breast cancer is the major public health problem throughout the world and it results in serious physical damages and death. This work proposes the use of joint model to study breast cancer in patients of Ayder Hospital. The primary motivation is to contribute to the understanding of the tumor cell progression of breast cancer, within Ayder Hospital, using a joint model that takes into account a possible existence of a serial correlation structure within a same subject observations from September 2015 till December 2018. The general aim of this study was to investigate the risk of longitudinal change in tumor cell level on time to death due to breast cancer among breast cancer patients. Hospital-based retrospective cohort study was conducted among breast cancer patients. A joint model of longitudinal and time to death model was used to determine the risk of longitudinal change in tumor cell level on time to death due to breast cancer patients. These were used by using JM package in R version. Results from joint models, showed that the longitudinal Tumor cell progression was signicantly associated with the survival probability of these patients(estimated association parameter(ɑ) in the joint model is 0.84 with corresponding (95% CI: 2.28,2.37). A comparison between parameter estimates obtained in this joint model and independent survival and longitudinal analysis lead us to conclude that independent analysis brings up bias parameter estimates. There is a strong association between the progression change in log(TCL) and risk of mortality due to breast cancer.http://dx.doi.org/10.1080/2331205X.2021.1874090breast cancertumor celllongitudinal analysissurvival analysisjoint model
spellingShingle Bsrat Tesfay
Tewodros Getinet
Endeshaw Assefa Derso
Joint modeling of longitudinal change in tumor cell level and time to death of breast cancer patients: In case of Ayder comprehensive specialized Hospital Tigray, Ethiopia
Cogent Medicine
breast cancer
tumor cell
longitudinal analysis
survival analysis
joint model
title Joint modeling of longitudinal change in tumor cell level and time to death of breast cancer patients: In case of Ayder comprehensive specialized Hospital Tigray, Ethiopia
title_full Joint modeling of longitudinal change in tumor cell level and time to death of breast cancer patients: In case of Ayder comprehensive specialized Hospital Tigray, Ethiopia
title_fullStr Joint modeling of longitudinal change in tumor cell level and time to death of breast cancer patients: In case of Ayder comprehensive specialized Hospital Tigray, Ethiopia
title_full_unstemmed Joint modeling of longitudinal change in tumor cell level and time to death of breast cancer patients: In case of Ayder comprehensive specialized Hospital Tigray, Ethiopia
title_short Joint modeling of longitudinal change in tumor cell level and time to death of breast cancer patients: In case of Ayder comprehensive specialized Hospital Tigray, Ethiopia
title_sort joint modeling of longitudinal change in tumor cell level and time to death of breast cancer patients in case of ayder comprehensive specialized hospital tigray ethiopia
topic breast cancer
tumor cell
longitudinal analysis
survival analysis
joint model
url http://dx.doi.org/10.1080/2331205X.2021.1874090
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