Iranian Journal of

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Background & Objectives: Cancer has been traditionally regarded as a fatal disease it is a major public health problem in many countries throughout the world. In recent years, cancer morbidity and mortality has increased in our country and notably stomach cancer now ranks second or third among all cancers types with regard to morbidity.
Methods: Our study included all gastric cancer patients registered in the cancer registry of Fars province. The patients' survival status was followed using phone calls and death records from hospitals, other medical centers, and the city's cemetery. Data analysis involved the use of the nonparametric Kaplan-Meier and Cox proportional hazards models and was performed with the software package SPSS V.13.
Results: Of the 442 patients with gastric cancer, 303 cases (68.6 percents) were male, and the mean age of patients was 58.41 years (SD=14.46). In univariate analysis with the KM method, a statistically significant association was found between survival rates and the following factors: age at diagnosis (P<0.001), tumor grade (P=0.009), presence of metastases (P<0.001), and type of the initial treatment (P=<0.001). Factors without a significant relationship with the survival rate included sex, ethnicity, weight, BMI, tobacco use, history of cancer in close or distant relatives, place of residence, number of children, marital status, occupation, and income. In Cox regression, only age at diagnosis, tumor grade, and the presence of metastases showed a significant association with survival rates.
Conclusions: Our results imply that early detection of cancer at a lower age and in lower tumor grades could be important for increasing the patients' life expectancy.

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Background & Objective: Using parametric models is common approach in survival analysis. In the recent years, artificial neural network (ANN) models have increasingly used in survival prediction. The aim of this study was to predict of survival rate of patients with gastric cancer by using a parametric regression and ANN models and compare these methods.
Methods: We used the data of 436 gastric cancer patients from a cancer registry in Tehran between 2002-2007. All patients had a confirmed diagnosis. Data were randomly divided into two groups: training and testing (or validation) set. For analysis of data we used a parametric model (exponential, Weibull, normal, lognormal, logistic and log-logistic models) and a three layer ANN model. In order to compare of the prediction of two models, we used the area under receiver operating characteristic (AUROC) curve, classification table and concordance index.
Results: The prediction accuracy of the ANN and the parametric (Weibull) models were 79.45% and 73.97% respectively. The AUROC for the ANN and the Weibull models were 0.815 and 0.748 respectively.
Conclusions: The ANN had a better predictions than the Weibull model. Thus it is suggested to use of the ANN model survival prediction in field of cancer.

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Background and Objectives: Each year almost 400,000 people are diagnosed with oesophageal cancer worldwide. Wide variation in incidence has been reported both between countries and in different ethnic groups and populations within a country. The area with the highest reported incidence for oesophageal cancer is the so-called Asian ‘oesophageal cancer belt’, which stretches from eastern Turkey through north-eastern Iran, northern Afghanistan and southern Russia to northern China. In the high risk area of Gonbad in Iran, world age-standardised rates are more than 200 per 100,000 and the male/female ratio is reported as 0.8:1.0.This study aimed to assess the risk factors and demographic factors influencing survival of patients with esophageal cancer in north of Iran using weibull and log-logistic regression models.
 Methods: Demographic and clinical data of 359 patients with confirmed diagnosis of esophageal cancer from Babol Cancer registry utilized for our model. parametric and weibull models were employed to analyze the data. The Akaike information criterion (AIC) was also considered as a criterion to select the best model(s). All p values as 0.05 were considered as statistically significant.
Results: The sample study consisted of 62.7% men and 37.3% women. Estimated survival rates in 1, 3, and 5 years following diagnosis were 23%, 15%, and 13% respectively. According to AIC criterion, the hazard rate of non-monotonic and rejection proportional hazards assumption (p<0.05), log-logistic model was more efficient than weibull model. Family history of having cancer in patients showed a significant difference in both models.
Conclusion: It is concluded that early detection of people with a family history of cancer can be effective as an important factor in reducing the risk of death in patients with esophageal cancer.

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Background & Objectives: Peritoneal dialysis is one of the most common types of dialysis in patients with renal failure. However multivariate analysis such as log- rank test and Cox have usually used to evaluate association of risk factors in survival of this group of patients, the aim of this study was to perform of Weibull, Gamma, Lognormal and Logistic Mixture cure models in survival analysis of these patients.
Methods: Data of 433 patients undergoing CAPD who registered in two centers in Tehran, Iran between 1997 to 2009 were used in this analysis. We investigated center, gender, age, cholesterol, Low Density Lipoprotein (LDL), High density lipoprotein (HDL), triglyceride, albumin, hemoglobin, creatinine, Fasting Blood Sugar (FBS), calcium and phosphorous as variables effect with Kaplan-Meier and cure model. CUREREGR module was used for survival analysis.
Results: Comparison of AIC (Akaike Information Criterion) of Weibull, Gama, Lognormal and Logistic Mixture cure models showed that Weibull distribution AIC is lower for almost all variables than other distributions. Weibull distribution has better fitness for data than others. In the multivariate Weibull model, age and albumin variables had significant effect on long-term survival of patients (P<0.01). Triglycerides effect on long-term survival had borderline (P = 0.065). Also HDL, FBS and calcium were significant on short term survival (P<0.01) but significance of LDL was borderline (P=0.088).
Conclusion: Cure models have the ability to analyze dialysis patients' survival data and can differentiate long-term survival from short- term survival. The interpretation of survival data with these statistical models could be more accurate and would help to make better prediction for patients by health care professionals.

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Background & Objectives: Identifying the important influential factors is a great challenge in oncology studies. Decision tree is one of methods that could be used to evaluate the prognostic factors and classifying the patients' homogeneously. This method identifies the main prognostic factors and then determines the subgroups of patients based on those prognostic factors. The aim of this study was to assess the prognostic factors and homogeneous subgroups of colorectal patient through survival tree.
Methods: Data collected from an observational of 739 colorectal patients registered in the cancer registry affiliated to the center of Research Center of Gastroenterology and Liver Disease (RCGLD), Shahid Beheshti Medical University, Tehran, Iran. Death was the interested event and the survival time was calculated from date of diagnosis until occurrence of event (or censoring) in months. Finally we used decision tree based method for classifying and analyzing the data.
Results: Based on our result, decision tree identified four covariates as important prognostic factors in 0.05 significant levels: general stage of cancer, age of diagnosis, histology of tumor and morphology type of tumor. Also patients based on these prognostic factors divided into five homogeneous subgroups. The greater values of measure of separation (SEP) criterion support the appropriateness of this model for such the data.
Conclusion: Decision tree is powerful and intuitive method. It has a key feature that is in addition to evaluate the prognostic factors, provides the homogeneous subgroups for future analysis.

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Background & Objectives: Although several studies have been carried out for evaluation of the first birth interval, none of them has considered the presence of infertile women within the sample. Therefore, the aim of this study was to employ survival analysis to study the first birth interval and its determinant factors more accurately.
Methods: In Data from 1068 married women of reproductive age in west Azarbaijan province were considered in this investigation. Two-stage sampling design was used to collect data via a questionnaire, modified Gompertz model, a special kind of cure models, was employed in this study. For descriptive and analytical data analysis, SPSS 16 and R 2.12 were used respectively.
Results: In this study, the average interval between marriage and first birth was 3.9± 0.7 (± SD) years. Using modified Gompertz model, among all demographic factors only mother’s education had significant effect on the first birth interval so that with increasing mother’s educational level, the first birth interval had also increased. (P =0.007). In addition, the estimation of the proportion of women who did not have any children was 0.062 that showed a positive trend with increasing mother’s educational level.
Conclusion: This study revealed that due to the presence of infertility among married women the use of Modified Cured Gompertz model is an appropriate method for evaluation of the first birth intervals and it's determinant factors.

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Background & Objectives: Although the population of elderly is increasing in Iran, few studies carried out on this group. The aim of this study was to identify life expectancy and contributory risk factors for the Tehranian elderly of ages above 60 years.
Methods: Individuals above 60 years old whom were recruited in the primary phase of the Tehran Lipid and Glucose Study (TLGS) during 1998-2001 were followed up for 12 years and their vital status were registered (1998-2011). Age and sex mortality rates for age groups (60-69, 70-79, 80+) were calculated and by using Cox proportional hazard model the mean of survival time and hazard rates with respect to risk factors were estimated.
Results: Life expectancy for females and males after crossing 60 years of age reaches to 81 and 80 years, respectively without any statistically significant differences between these two groups. Cox model showed that diabetes, BMI>33Kg/m2 and non ischmecic heart disease reduced survival time in women significantly. While diabetes, smoking, hypertension, ischemic heart disease, history of MI, stroke or sudden death of father, brother or son, lack of physical activity and antihypertensive medications are among the hazardous risk factors for men.
 Conclusion: Among the variables studied, only three (ABC) of them were found as risk factors of women's life, while for men seven risk factors were identified. It seems that more studies are needed to determine the risk factors for women.

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  Background & Objectives : Gastric cancer has a low survival and remains a serious threat to the health of human life, especially in developing countries such as Iran. The present study was performed to estimate the main effective factors in the survival rate of patients with gastric cancer in the Province of Kohgilouyeh & Boyerahmad.

  Methods: All cases of gastric cancer in Kohgiloyeh and Boyerahmad recorded in Provinces of Fars and Kohgiloyeh and Boyerahmad cancer registry were enrolled in this study. The impact of the independent variables on the survival was estimated by single and multivariate Cox regression controlled for the probable confounding variables. Survival analysis was performed using Kaplan Meier curves, the log-rank test, and Wilcoxon test to compare the results. Analysis of the data was performed by SPSS 19, and P-values less than 0.05 were considered significant.

 Results: Among the 348 studied patients, 75.6% were male and the rest (24.4%) were female. In general, in this study, 1, 2, 3, 4, and 5-year survival rate of the patients was 37, 27, 20, 19, and 18%, respectively. By combining these end variables in regression models, three risk groups were identified. In the high risk group, the cumulative survival rate was 0% at the end of the fifth year.

 Conclusion: Execution of the down-staging program through public education, considering the low survival rate in this province seems essential especially for high-risk groups such as farmers, ranchers and regional nomadic populations.

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  Background & Objectives : Management of time-dependent variables is the advantages of survival analysis. This study compares time-dependent and -independent variables in survival analysis in culling of dairy cows.

  Methods: In this historical cohort, 7067 dairy cows in the Province of Tehran were recruited. Cows were followed to the next calving or culling. Data on the occurrence of health disorders, calving season, parity, and milk production was obtained. Model 1 treated diseases as time-independent covariates. In models 2, up to 5 diseases were considered time-dependent covariates. For each observation, we split follow-up time in intervals each corresponding to a different lactation month using Lexis expansion of the original dataset. Model 2 assumed that an animal experienced a certain disease from the beginning of the occurrence of that disease by the end of the period. Model 3 assumed that cows were at risk from the begging of the study until the disease occurred (inverse of model 2). In models 4 and 5, an animal was assumed to experience a certain disease for 1 month if the disease occurred during this period. In Model 4 assumed diseases occurred only one time, and in model 5, multiple disease occurrences at different months were considered as different episodes.

  Results : AIC in model 1 and 5 was 10809 and 10366 moreover, BIC was 10926 and 10528. According to this numbers and the shape of the Cox-Snell Residuals, model 5 with Gompertz distribution was the best model.

  Conclusion : Models without time dependency tended to seriously underestimate the risk of a disease on culling.

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Background and Objectives: A small sample size can influence the results of statistical analysis. A reduction in the sample size may happen due to different reasons, such as loss of information, i.e. existing missing value in some variables. This study aimed to apply bootstrap and jackknife resampling methods in survival analysis of thalassemia major patients.

Methods: In this historical cohort study, the data of 296 patients with thalassemia major who were visited at Zafar Clinic, Tehran, from 1994 to 2013 were used. Parametric survival models were used to analyze the data. The log – normal survival model was selected as the best model and then the bootstrap and jackknife resampling algorithms were used for this model. Data analysis was carried out with the STATA 12.0 software.

Results: The results of the resampling methods showed that standard errors decreased and confidence intervals were shortened. In addition, the result of the bootstrap and jackknife resampling methods showed that age group and the relationship of the parents (P<0.001) were significant compared with the log-normal model (P>0.900).

Conclusion: Comparison of the confidence intervals suggests that the jackknife resampling method can be used when the sample size is small.

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Background and Objectives: Neuropathy is a common complication of diabetes that can cause disability in diabetic patients. The aim of this study was to determine of effective factors in the Event Time of neuropathy in type 2 diabetic patients using the Cox proportional hazards model.

Methods: This study included 371 patients with type II diabetes without neuropathy who were registered at Fereydunshahr Diabetes Clinic. Subjects were followed up for the development of neuropathy between 2006 until March 2016. The data were analyzed using the R software (ver. 3.2.3). The test was conducted at an error level of 5%.

Results: At the end of 10 years of study, the cumulative incidence and prevalence of neuropathy was 30.7% and 41.6%, respectively. The Kaplan-Meier method showed the mean time to detection of neuropathy was 76.6 ± 5 months after the first diagnosis of diabetes (83.8 ± 8 in men and 72.7 ± 6 in women). The semi-parametric Cox regression model revealed the one-year, two-year, five-year, and eight-year disease-free survival was 0.867, 0.819, 0.647, and 0.527, respectively. Also, four variables of duration of diabetes, sex, family history of diabetes, and HbA1c can be considered as strong determinants of the time of development of neuropathy in the semi-parametric model (COX) (P<0.05).

Conclusion: Optimal glycemic control and regular evaluation of legs in elderly patients, especially women with a positive family history, decrease the occurrence and progression of neuropathy and improve the quality of life in diabetic patients.

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Background and Objectives: Tuberculosis is the most common cause of death among single-factor infectious diseases and is the tenth cause of death among all diseases in the world. The disease is spread mainly from an infected person through close contact with other people living in one place. The aim of this study was to investigate the relationship between the spatial correlation structure and the recovery time of patients with pulmonary tuberculosis in Iran.
Methods: In this applied study, the data of 20554 patients with sputum smear-positive pulmonary tuberculosis in Iran from 1389 to 1393 were used. A parametric accelerated failure time model with spatial frailty and batesian approach was used to analyze the data. The OpenBUGS 1.4 was used for programming and the ArcGIS 9.2 was used for mapping the environmental impact on tuberculosis.
Results: The mean age of the patients was 50.35 years with a standard deviation of 21.6 years. The results showed that the geographical environment, gender, prison condition, degree of smear positivity at diagnosis and location (urban-rural) had a significant impact on the recovery time of pulmonary tuberculosis patients. The recovery time of patients with smear grade 1-9 bacilli, 1+ and 2+ who were treated was significantly shorter than the others.
Conclusion: According to the study, geographical environment and the location have a significant impact on smear positive patients’ recovery time. This impact depends on the degree of smear positivity in some provinces and is independent of it in some other provinces.

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Background and Objectives: The Cox model is one of the methods used in survival data; however, the use of hierarchical data, such as the data of this study, violates the assumption of independence, the the Cox model cannot be used  assuming independence of observations. One of the important methods for analyzing survival hierarchy data is the use of the multilevel Cox model. In this method, in addition to modeling the response variable, regression coefficients are also modeled and the measurement error resulting from the lack of data independence is reduced. The present study used a multilevel Cox model to investigate the effect of the retention of antihypertensive drugs in people with hypertension.
 
Methods: This longitudinal survival study was conducted in 346 workers with hypertension in Mobarakeh Steel Company in Isfahan. During the years 1390-1394, when the staff attended the health center in the factory, they were treated with six drugs, including captopril, losartan, atenolol, propranolol, amlodipine, and hydrochlorothiazide. In order to examine the relationship of the retention of drugs with job experience, body mass index, and drug history, a two-level Cox model was used as h_ij (t)=h_0 (t)exp⁡(α_g+x_ij β_j), where i and j is the first and second level units, respectively.
 
Results: During five years, the findings of model fitting showed the effect of body mass index (P = 0.019), atenolol (P=0.046), and amlodipine (P=0.021) in a single form, and the effect of losartan-amlodipine ¬(P=0.042) and atenolol-hydrochlorothiazide (P = 0.003) in a combination form were significant.
 
Conclusion: Based on the study results, the most effective drugs for hypertension control are amlodipine monotherapy, amlodipine-losartan combination therapy, and atenolol monotherapy, respectively.

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Background and Objectives: In many medical situations, people can experience recurrent events with a terminal event. If the terminal event is considered a censor in this type of data, the assumption of independence in the analysis of survival data may be violated. This study was conducted to investigate joint modeling of frequent events and a final event (death) in breast cancer patients using a frailty model.
 
Methods: In this retrospective cohort study, 443 patients with breast cancer registered at the Hospital of Shohadaye Tajrish Cancer Research Center were studied. The model of Liu (2004) was applied for joint modeling of recurrent events and a terminal event in which a shared frailty with gamma-distribution was used. Data modeling and data analysis were done using the R software.
 
Results: Four hundred and forty three women with breast cancer were studied. Univariate and multivariate analysis were performed in these patients. Of these, 338 cases (76.3%) had recurrence events, and 105 (23.7%) were censored. The obtained results of joint frailty model indicated that the relative risk of relapse in patients with a positive first-degree family history was 36% higher than that of other people (P<0.05). The relative risk of relapse in patients with stage 3 disease was 19% more than other stages and also the relative risk of relapse in patients with chemotherapy was 2.5 times higher than those without chemotherapy.
 
Conclusion: In this study, the presented model, in addition to simultaneous modeling capability of the event, could help prevent a higher prevalence of the terminal event (death) and thus reduce the adverse effects of reversible diseases.

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Background and Objectives: The incidence of rectal cancer is increasing in developing societies, especially in younger age groups. The aim of this study was to evaluate the factors affecting the survival of patients with rectal cancer in the presence of competing risks.
 
Methods: In this retrospective cohort study, the data of 121 patients with rectal cancer during 2001-2017 were studied. Death related to cancer progression was considered as the interest outcome and other causes of death were considered as competing risks. Cause-specific and sub-distribution hazard models were used to investigate the factors affecting patient survival in the presence of competing risk.
 
Results: The mean (SD) age of the patients was 53.4 (13.9) years and 68 patients (56.2%) were male. The results of log-rank test showed that sex, age, metastasis, type of first treatment, rate of penetration into intestinal wall, tumor location, number of lymphomas involved and tumor size had significant effects on the patient survival (P<0.05). Based on cause-specific and sub-distribution hazard models, tumor stage, lymph node metastasis, and tumor grade had significant effects on death hazard due to the cancer progression (P<0.05).
 
Conclusion: Due to the need to consider competing risks, the results of both competing risk methods showed that tumor grade, lymph node metastasis and stage increased the instantaneous hazard and hazard of cancer death. Therefore, to determine the specific risk factors for each cause of death in the survival analysis, competing risk methods should be used if there is more than one cause of death.

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Background and Objectives: This study aimed to investigate the effective factors in the survival/hazard time of Covid-19 patients in three waves of epidemic.
 
Methods: All 880 Covid-19 patients were included in this prospective cohort study using the census method. Polymerase chain reaction was used to diagnose Covid-19. The survival status of these patients was followed up for 4 months. The analysis of this study was based on the time of infection in three epidemic waves in IRAN. To analyze the data, the Kaplan-Meier nonparametric approach and Cox proportional hazards regression model were used. Survival distributions were compared in three epidemic waves using the R software (version 3.6.2) (P<0.05).
 
Results: We diagnosed 880 positive case of Covid-19 using PCR test on 2269 susspected people who had respiratory symptomps. At the end of 1-year follow-up, cumulative incidence (risk) of Covid-19 was estimated 7%. Effective factors in the survival time of patients with Covid-19 based on Cox multivariate regression model were: 1- Age 2- Intensity of infection (Hospitalization) 3- Heart disease 4- Epidemic Wave and 5- Transmission mode of the disease (P <0.05). The Kaplan-Meier approach and log rank test (Mantel-Cox) showed a significant difference in the survival rate in three epidemic waves (P = 0.018).
 
Conclusion: Elderly patients, especially those with a history of heart disease, are at higher risk of death than other groups. In addition to regular screening, these patients will need active monitoring, especially at the time of hospitalization.

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