ArticleBMC medical research methodology2022
A bayesian approach to model the underlying predictors of early recurrence and postoperative death in patients with colorectal Cancer.
Article in BMC medical research methodology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 13 citations in OpenAlex.
- A novel risk prediction nomogram for early death in patients with resected synchronous multiple primary colorectal cancer based on the SEER database.International journal of colorectal disease · 2023Trial
- Developing a Clinician-Friendly Online Dynamic Nomogram for Survival Prediction in Colon Cancer Patients.Annals of gastroenterological surgery · 2026Article
- Significance of serum APE1-AAbs, PTX-3, and miR-486-3p in patients with colorectal cancer undergoing radical surgery.World journal of gastrointestinal oncology · 2025Article
- Prognostic factors and survival disparities in right-sided versus left-sided colon cancer.Scientific reports · 2024Article
- Cumulative Incidence, Risk Factors, and Overall Survival of Disease Recurrence after Curative Resection of Stage II-III Colorectal Cancer: A Population-based Study.Cancer research communications · 2024Article
- Exploring the impact of stage and tumor site on colorectal cancer survival: Bayesian survival modeling.Scientific reports · 2024Article
- Survival prediction and prognostic factors in colorectal cancer after curative surgery: insights from cox regression and neural networks.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis 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).
methodsIn 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).
resultsThe 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
conclusionAccording 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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