Leila Mahmoudi1, Ramezan Fallah1, Ghodratollah Roshanaei2, Mohammad Asghari-Jafarabadi3,4,5. 1. Department of Statistics and Epidemiology, School of Medicine, Zanjan University of Medical Sciences, Mahdavi Blvd, 4513956111, Zanjan, Iran. 2. Modeling of Non-communicable Diseases Research Canter, Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran. 3. Department of Statistics and Epidemiology, School of Medicine, Zanjan University of Medical Sciences, Mahdavi Blvd, 4513956111, Zanjan, Iran. m.asghari862@gmail.com. 4. Cabrini Research, Cabrini Health, Melbourne, VIC, 3144, Australia. m.asghari862@gmail.com. 5. School of Public Health and Preventative Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, 3800, Australia. m.asghari862@gmail.com.
Abstract
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 ), T3 stage (0601: 0.530-0.713), N2 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), T3 stage (0.363: 0.592 - 0.301), T4 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.
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 ), T3 stage (0601: 0.530-0.713), N2 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), T3 stage (0.363: 0.592 - 0.301), T4 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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