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A bayesian approach to model the underlying predictors of early recurrence and postoperative death in patients with colorectal Cancer

OBJECTIVE: This study aimed at utilizing a Bayesian approach semi-competing risks technique to model the underlying predictors of early recurrence and postoperative Death in patients with colorectal cancer (CRC). METHODS: In this prospective cohort study, 284 patients with colorectal cancer, who und...

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Autores principales: Mahmoudi, Leila, Fallah, Ramezan, Roshanaei, Ghodratollah, Asghari-Jafarabadi, Mohammad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9555178/
https://www.ncbi.nlm.nih.gov/pubmed/36224555
http://dx.doi.org/10.1186/s12874-022-01746-y
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author Mahmoudi, Leila
Fallah, Ramezan
Roshanaei, Ghodratollah
Asghari-Jafarabadi, Mohammad
author_facet Mahmoudi, Leila
Fallah, Ramezan
Roshanaei, Ghodratollah
Asghari-Jafarabadi, Mohammad
author_sort Mahmoudi, Leila
collection PubMed
description OBJECTIVE: This study aimed at utilizing a Bayesian approach semi-competing risks technique to model the underlying predictors of early recurrence and postoperative Death in patients with colorectal cancer (CRC). METHODS: In this prospective cohort study, 284 patients with colorectal cancer, who underwent surgery, referred to Imam Khomeini clinic in Hamadan from 2001 to 2017. The primary outcomes were the probability of recurrence, the probability of Mortality without recurrence, and the probability of Mortality after recurrence. The patients ‘recurrence status was determined from patients’ records. The Bayesian survival modeling was carried out by semi-competing risks illness-death models, with accelerated failure time (AFT) approach, in R 4.1 software. The best model was chosen according to the lowest deviance information criterion (DIC) and highest logarithm of the pseudo marginal likelihood (LPML). RESULTS: The log-normal model (DIC = 1633, LPML = -811), was the optimal model. The results showed that gender(Time Ratio = 0.764: 95% Confidence Interval = 0.456–0.855), age at diagnosis (0.764: 0.538–0.935 ), T(3) stage (0601: 0.530–0.713), N(2) stage (0.714: 0.577–0.935 ), tumor size (0.709: 0.610–0.929), grade of differentiation at poor (0.856: 0.733–0.988), and moderate (0.648: 0.503–0.955) levels, and the number of chemotherapies (1.583: 1.367–1.863) were significantly related to recurrence. Also, age at diagnosis (0.396: 0.313–0.532), metastasis to other sites (0.566: 0.490–0.835), T(3) stage (0.363: 0.592 − 0.301), T(4) stage (0.434: 0.347–0.545), grade of differentiation at moderate level (0.527: 0.387–0.674), tumor size (0.595: 0.500–0.679), and the number of chemotherapies (1.541: 1.332–2.243) were the significantly predicted the death. Also, age at diagnosis (0.659: 0.559–0.803), and the number of chemotherapies (2.029: 1.792–2.191) were significantly related to mortality after recurrence. CONCLUSION: According to specific results obtained from the optimal Bayesian log-normal model for terminal and non-terminal events, appropriate screening strategies and the earlier detection of CRC leads to substantial improvements in the survival of patients.
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spelling pubmed-95551782022-10-13 A bayesian approach to model the underlying predictors of early recurrence and postoperative death in patients with colorectal Cancer Mahmoudi, Leila Fallah, Ramezan Roshanaei, Ghodratollah Asghari-Jafarabadi, Mohammad BMC Med Res Methodol Research OBJECTIVE: This study aimed at utilizing a Bayesian approach semi-competing risks technique to model the underlying predictors of early recurrence and postoperative Death in patients with colorectal cancer (CRC). METHODS: In this prospective cohort study, 284 patients with colorectal cancer, who underwent surgery, referred to Imam Khomeini clinic in Hamadan from 2001 to 2017. The primary outcomes were the probability of recurrence, the probability of Mortality without recurrence, and the probability of Mortality after recurrence. The patients ‘recurrence status was determined from patients’ records. The Bayesian survival modeling was carried out by semi-competing risks illness-death models, with accelerated failure time (AFT) approach, in R 4.1 software. The best model was chosen according to the lowest deviance information criterion (DIC) and highest logarithm of the pseudo marginal likelihood (LPML). RESULTS: The log-normal model (DIC = 1633, LPML = -811), was the optimal model. The results showed that gender(Time Ratio = 0.764: 95% Confidence Interval = 0.456–0.855), age at diagnosis (0.764: 0.538–0.935 ), T(3) stage (0601: 0.530–0.713), N(2) stage (0.714: 0.577–0.935 ), tumor size (0.709: 0.610–0.929), grade of differentiation at poor (0.856: 0.733–0.988), and moderate (0.648: 0.503–0.955) levels, and the number of chemotherapies (1.583: 1.367–1.863) were significantly related to recurrence. Also, age at diagnosis (0.396: 0.313–0.532), metastasis to other sites (0.566: 0.490–0.835), T(3) stage (0.363: 0.592 − 0.301), T(4) stage (0.434: 0.347–0.545), grade of differentiation at moderate level (0.527: 0.387–0.674), tumor size (0.595: 0.500–0.679), and the number of chemotherapies (1.541: 1.332–2.243) were the significantly predicted the death. Also, age at diagnosis (0.659: 0.559–0.803), and the number of chemotherapies (2.029: 1.792–2.191) were significantly related to mortality after recurrence. CONCLUSION: According to specific results obtained from the optimal Bayesian log-normal model for terminal and non-terminal events, appropriate screening strategies and the earlier detection of CRC leads to substantial improvements in the survival of patients. BioMed Central 2022-10-12 /pmc/articles/PMC9555178/ /pubmed/36224555 http://dx.doi.org/10.1186/s12874-022-01746-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Research
Mahmoudi, Leila
Fallah, Ramezan
Roshanaei, Ghodratollah
Asghari-Jafarabadi, Mohammad
A bayesian approach to model the underlying predictors of early recurrence and postoperative death in patients with colorectal Cancer
title A bayesian approach to model the underlying predictors of early recurrence and postoperative death in patients with colorectal Cancer
title_full A bayesian approach to model the underlying predictors of early recurrence and postoperative death in patients with colorectal Cancer
title_fullStr A bayesian approach to model the underlying predictors of early recurrence and postoperative death in patients with colorectal Cancer
title_full_unstemmed A bayesian approach to model the underlying predictors of early recurrence and postoperative death in patients with colorectal Cancer
title_short A bayesian approach to model the underlying predictors of early recurrence and postoperative death in patients with colorectal Cancer
title_sort bayesian approach to model the underlying predictors of early recurrence and postoperative death in patients with colorectal cancer
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9555178/
https://www.ncbi.nlm.nih.gov/pubmed/36224555
http://dx.doi.org/10.1186/s12874-022-01746-y
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