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 & Objectives: Although Cox regression is commonly used to detect relationships between patient survival and demographic/clinical variables, there are situations where parametric models can yield more accurate results. The objective of this study was to compare two survival regression methods, namely Cox regression and parametric models, in patients with gastric carcinoma registered at Taleghani Hospital, Tehran.
Methods: Using data from 746 patients who had received care at Taleghani Hospital from February 2003 through January 2007, we compared survival rates between different patient groups with both parametric methods and Cox regression models. The former group included Weibull, exponential and log-normal regression we used the Akaike Information Criterion (AIC) and standardized parameter estimates to compare the efficiency of various models. All the analyses were performed with the SAS software and the level of significance was set at P< 0.05.
Results: The results showed a significantly higher chance of survival in the following subgroups: those with age at diagnosis < 35 years, lower tumor size and those without metastases (P< 0.05). According to AIC, Cox and exponentials model are similar in multivariate analysis but in univariate analysis parametric models are more efficient than Cox, except in the case of tumor size. Log-normal appears to be the best model.
Conclusions: Cox and exponential models have similar performance in multivariate analysis. However, it seems that there is no single model that performs substantially better than others in univariate analysis. The data strongly supported the log-normal regression among parametric models it can give more precise results and can be used as an alternative for Cox in survival analysis of patients with gastric cancer.

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Background & Objectives: Despite decreasing the trend of coronary artery diseases in developed countries and outstanding improvements in clinical management of these patients, case fatality rate after an acute myocardial infarction (AMI) remains high in both genders. Identifying predicting factors of short-term survival in patients with AMI may play an important role in reducing mortality in these patients.
Methods: In this cohort study, all patients with acute myocardial infarction (AMI) admitted to all hospitals in Isfahan, Iran, during 2000-2008 which registered in Isfahan cardiovascular research Institute were analyzed. We used Cox regression models, uni- and multi-variable analysis. 
Results: Within the study period, 8800 AMI patients (73.6% male) were admitted with mean age of 61.85±12.5, and overall 28-day survival of 90.5%. Relative risk (RR) of death for 50-70 years old patients was 2.5 (CI:2-3.1), for over 70 years old RR=5 (CI:4-6.3), for women RR=1.7 (CI:1.5-1.9), for patients who had not received streptokinase RR=0.9 (CI:0.8-1.1), for inferior MI RR=4.2 (CI:2.2-7.8) and for anterior MI, RR was equal to 7.2 (CI:4-13.3).
Conclusion: Recognizing the predicting factors of short-term survival of AMI patients may help health professionals to provide better healthcare services for more at risk patients, i.e. elderly, women and patients with an anterior MI.

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Background & Objectives: Cox regression model is one of the statistical methods in survival analysis. The use of smoothing techniques in Cox model makes the more accurate estimates for the parameters. Fractional polynomial is one of these techniques in Cox model. The aim of this study was to assess the effects of prognostic factors on survival of patients with gastric cancer using the fractional polynomial in Cox model and Cox proportional hazards.
Methods: Information of total of 216 patients with gastric cancer who underwent surgery in the gastroenterology ward of Taleghani Hospital in Tehran between 2003 and 2008 were included in this retrospective study. In this research, fractional polynomial in Cox model and Cox proportional hazards model were utilized for determining the effects of prognostic factors on patients’ survival time with gastric cancer. The SPSS version 18.0 and R version 2.14.1 were used for data analysis. These models were compared with Akaike information criterion.
 Results: The analysis of Cox proportional hazards and fractional polynomial models resulted in age at diagnosis and tumor size as prognostic factors on survival time of patients with gastric cancer independently (P<0.05). Also, Akaike information criterion was equal in both models.
Conclusion: In the present study, the Cox proportional hazards and fractional polynomial models led to similar results with equal Akaike information criterions. Using of smoothing methods helped us eliminate non-linear effects but it seemed more appropriate to use Cox proportional hazards model in medical data because of its’ ease of interpretation and capability of modeling in both continuous and discrete covariates.

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  Background & Objectives : The length of stay (LOS) is a useful indicator that can be used according to the objective to improve hospital care performance. The purpose of our study was to find factors affecting infants LOS in NICU at Children's Medical Center, Imam Khomeini Hospital, Tehran University of Medical Sciences, using the Cox multiple hazards regression model.

  Methods : This historical cohort study reviewed 369 medical records of all NICU admitted newborns at Children's Medical Center in 2009. The required data were collected through a data collection form. The Cox multiple hazards regression model was used to determine the factors affecting LOS in infants who were discharged on the physician‘s order.

 Results: The median of stay in NICU was 9 days. Of 369 infants, 272 were discharged with improvement. The results of multiple Cox proportional hazards regression model showed the following factors were associated with LOS in the NICU: prematurity, referral from other hospitals, gastrointestinal diseases and infections, central venous catheterization, mechanical ventilation, and antibiotic therapy (P < 0.05).

  Conclusion : Cox proportional hazards regression model should be used when the dependent variable is time and we have censored data. Improving prenatal health care, constructing NICU in hospitals with high risk labor, reduction of preterm birth risk factors, and improving primary health-care services can help us to reduce LOS in NICU.

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Background & Objectives: The standard methods for the comparison of two drugs in a randomized controlled clinical trial in the presence of non-compliance are intention-to-treat or per-protocol approaches. Both approaches have problems with estimation of drug effects, and researchers are not still certain to adopt which one. In this study, the bias of intention-to-treat and per-protocol approaches was calculated using Monte-Carlo simulation. We tried to choose the best approach (based on the AIC index) for comparing Risperidone plus Celecoxib and Risperidone plus Placebo.

Methods: This secondary study was conducted to compare the effect of Risperidone plus Celecoxib and Risperidone plus Placebo among 60 schizophrenic patients. To choose between the intention-to-treat and per-protocol approaches, Monte-Carlo simulation with Ackaike (AIC) and Baysian (BIC) indices was used.

Results: The results of Monte-Carlo simulation showed that when the sample size was small (n=30 or n=60) under fixed conditions of non-compliance equal to 5% and 10%, intention-to-treat had a better goodness of fit than per-protocol based on AIC and BIC. However, increasing the sample size in active and placebo groups (e.g., n=100) showed that per-protocol had a better goodness of fit than intention-to-treat.

Conclusion: When the sample size is large, the per-protocol approach may have a better goodness of fit than intention-to-treat to address the effects of non-compliance in randomized clinical trials.

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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 and Objectives: Q fever is a zoonosis with a worldwide distribution; this disease is a public health concern in many countries. The aim of this study was to determine the association between risk factors with Q fever seropositivity among veterinarians and vet staff in Southern Khorasan.

Methods: Questionnaires were prepared and 92 blood samples were obtained from all veterinary staff in the South Khorasan (East of Iran). The serum samples were tested with an indirect ELISA kit (anti body phase II); then, SPSS version 19.0 was employed to analyze the data using descriptive statistics and a confidence interval of 95%, chi-square test, and logistic regression.

Results: The results showed that 50 serum samples (54.35%) were positive and the results of data analysis with logistic regression indicated an association between seropositivity and contact with animal secretions and abortive materials. There was no correlation between seroprevalence and age, gender, work experience, education, use of unpasteurized dairy products, knowledge of disease, and clinical findings.

Conclusion: In conclusion, the present study showed a high seropositivity rate among vet staff which indicates that further attention should be paid to this disease in these groups.

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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: In survival analysis, using the Cox model to determine the effective factors requires the assumptions whose failure of leads to biased results. The aim of this paper was to determine the factors affecting the survival of metastatic gastric cancer patients using the non-parametric method of Randomized Survival Forest (RSF) model and to compare its result with the Cox model.
 
Methods: In this retrospective cohort study, 201 patients with metastatic gastric cancer were evaluated in Hamadan Province. Patient survival was calculated from diagnosis to death or end of study. Demographic characteristics (such as gender and age) and clinical variables (including stage, tumor size, etc.) were extracted from the patient records. Factors affecting survival were determined using the Cox model and RSF. Data analysis was performed using the R3.4.3 software and RandomForestSRC and survival packages.
 
Results: The mean (SD) age of patients was 61.5 (12.9) years old. The Cox model showed that chemotherapy (p=0.033) was effective in survival, and the results of fitting the RSF model showed that the most important variables affecting survival were type of surgery, location of metastasis, chemotherapy, age, tumor grade, surgery, number of involved lymph nodes, sex and radiotherapy. Based on the model appropriateness, the RSF model with log-rank split rule had a better performance compared to the Cox model.
 
Conclusion: If the number of variables is high and there is a relationship between the variables, the RSF method identifies the important and effective variables on survival with high accuracy without requiring restrictive assumptions compared to the Cox model.

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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.