Determining the Effective Factors on Gastric Cancer Using Frailty Model in South-East and North of Iran

Nikaeen, Roja; Khalilian, Alireza; Bahrampour, Abbas

doi:10.29252/jhs.5.3.35

Cited by 3 publications

(3 citation statements)

References 39 publications

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“…32 Conversely, the results of the study in Iran using the Frailty model showed no significant relationship between opium use and survival rate. 33 The limitations of the present study were incomplete information and a low sample size. On the other hand, the strength of this study is the results obtained using MSMs.…”

Section: Discussionmentioning

confidence: 76%

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Survival Analysis of Gastric Cancer Patients Using Illness-Death Model In Kerman City, Southeast Iran during 2001-2016

Moghadam

Mirzaee

Bahrampour

et al. 2021

Middle East J Dig Dis

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BACKGROUND Gastric cancer (GC) is the fifth most common cancer and the third most common cause of cancer death worldwide. The aim of this study was to investigate the factors affecting the survival of patients with GC using the illness-death model (metastasis as an intermediate event). METHODS In this retrospective cohort study, 339 patients with GC who were referred to Shahid Bahonar and Afzalipour Hospitals in Kerman, Southeast Iran during 2001-2016 were included. Demographic, therapeutic, and clinical data were collected from the patients’ medical records. To evaluate the factors affecting patients’ survival and the relationship between the factors, the illness-death model (metastasis as an intermediate event) was used. RESULTS One, three, and five-year survival rates in patients with GC were estimated to be 63%, 40%, and 30%, respectively. The results of analysis of illness-death model showed that age (HR = 0.98, 95% CI: 0.97- 0.99; p = 0.007) and histological grade (HR = 1.77; 95% CI: 1.68- 2.67; p =0.007) affected metastasis whereas history of cigarette smoking (HR = 1.89; 95% CI: 1.08- 3.3; p = 0.02) and chemotherapy (HR = 0.63; 95% CI: 0.4-0.93; p = 0.02) affected death hazard without metastasis. History of opium use (HR=2.11; 95% CI: 1.17- 3.8; p = 0.002), family history of GC (HR = 2.48; 95% CI: 1.2-5.15; p = 0.01) and histological grade (HR = 1.85; 95% CI: 1.11- 3.08; p = 0.02) were identified as factors affecting death hazard in patients with metastasis. CONCLUSION According to the results, patients’ age at the time of diagnosis and histological grade have a significant effect on the occurrence of metastasis. In addition, using the disease-death model, a complete understanding of the behavior and effects of the studied variables on different disease states can be realized.

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Section: Discussionmentioning

confidence: 76%

“…32 Conversely, the results of the study in Iran using the Frailty model showed no significant relationship between opium use and survival rate. 33 …”

Section: Discussionmentioning

confidence: 99%

Survival Analysis of Gastric Cancer Patients Using Illness-Death Model In Kerman City, Southeast Iran during 2001-2016

Moghadam

Mirzaee

Bahrampour

et al. 2021

Middle East J Dig Dis

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“…In survival analysis, this random effect is known as frailty. 13–15 In studies of frailty models, the continuous frailty random variable is usually used. One of the most common and widely used is gamma distribution continuous distributions.…”

Section: Introductionmentioning

confidence: 99%

Bayesian mixture cure rate frailty models with an application to gastric cancer data

Karamoozian

Baneshi

Bahrampour

2020

Stat Methods Med Res

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Mixture cure rate models are commonly used to analyze lifetime data with long-term survivors. On the other hand, frailty models also lead to accurate estimation of coefficients by controlling the heterogeneity in survival data. Gamma frailty models are the most common models of frailty. Usually, the gamma distribution is used in the frailty random variable models. However, for survival data which are suitable for populations with a cure rate, it may be better to use a discrete distribution for the frailty random variable than a continuous distribution. Therefore, we proposed two models in this study. In the first model, continuous gamma as the distribution is used, and in the second model, discrete hyper-Poisson distribution is applied for the frailty random variable. Also, Bayesian inference with Weibull distribution and generalized modified Weibull distribution as the baseline distribution were used in the two proposed models, respectively. In this study, we used data of patients with gastric cancer to show the application of these models in real data analysis. The parameters and regression coefficients were estimated using the Metropolis with Gibbs sampling algorithm, so that this algorithm is one of the crucial techniques in Markov chain Monte Carlo simulation. A simulation study was also used to evaluate the performance of the Bayesian estimates to confirm the proposed models. Based on the results of the Bayesian inference, it was found that the model with generalized modified Weibull and hyper-Poisson distributions is a suitable model in practical study and also this model fits better than the model with Weibull and Gamma distributions.

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Survival analysis in gastric cancer: a multi-center study among Iranian patients

et al. 2020

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Background Gastric cancer (GC) has been considered as the 5th most common type of cancer and the third leading cause of cancer-associated death worldwide. The aim of this historical cohort study was to evaluate the survival predictors for all patients with GC using the Cox proportional hazards, extended Cox, and gamma-frailty models. Methods This historical cohort study was performed according to documents of 1695 individuals having GC referred to three medical centers in Iran from 2001 to 2018. First, most significant prognostic risk factors on survival were selected, Cox proportional hazards, extended Cox, gamma-frailty models were applied to evaluate the effects of the risk factors, and then these models were compared with the Akaike information criterion. Results The age of patients, body mass index (BMI), tumor size, type of treatment and grade of the tumor increased the hazard rate (HR) of GC patients in both the Cox and frailty models ( P < 0.05). Also, the size of the tumor and BMI were considered as time-varying variables in the extended Cox model. Moreover, the frailty model showed that there is at least an unknown factor, genetic or environmental factors, in the model that is not measured ( P < 0.05). Conclusions Some prognostic factors, including age, tumor size, the grade of the tumor, type of treatment and BMI, were regarded as indispensable predictors in patients of GC. Frailty model revealed that there are unknown or latent factors, genetic and environmental factors, resulting in the biased estimates of the regression coefficients.

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Determining the Effective Factors on Gastric Cancer Using Frailty Model in South-East and North of Iran

Cited by 3 publications

References 39 publications

Survival Analysis of Gastric Cancer Patients Using Illness-Death Model In Kerman City, Southeast Iran during 2001-2016

Survival Analysis of Gastric Cancer Patients Using Illness-Death Model In Kerman City, Southeast Iran during 2001-2016

Bayesian mixture cure rate frailty models with an application to gastric cancer data

Survival analysis in gastric cancer: a multi-center study among Iranian patients

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