جستجوی مقالات مرتبط با کلیدواژه « تحلیل بقاء » در نشریات گروه « پزشکی »

 In analyzing time-to-event data, it is frequent that a proportion of individuals will never experience the event of interest. Using the cured models instead of standard ones for survival analysis will result in accurate estimates for predicting survival time. This study aimed to estimate the effect of potential risk factors on long-term and short-term survival of breast cancer patients.

 The study included 770 women diagnosed with early-stage breast cancer and treated with either breast conserving therapy or modified radical mastectomy with post-surgical radiation between 2005 and 2015. All study participants were followed up until September 2022. The review of the medical records of these patients was carried out at Shahid Sadoughi Hospital and the Ramazan Zadeh Radiotherapy Center in Yazd. Kaplan-Meier method was used to estimate disease-free survival and distant metastatic-free survival rates. Distant metastasis and prognostic factors were modeled by Cox proportional hazards and Semiparametric Mixture Cure models. Data analysis was done by Rstudio software.

 In this study, 218(28.3%) patients died, and metastasis occurred in 198(25.7%) patients until the end of the follow-up period. Based on the univariable and multivariable Semiparametric Mixture Cure model, the stage of disease was significant, and the risk of short-term distant metastatic-free survival for patients with stage IIIa was 2.7 times higher than patients with stage I and II of disease through multivariable Semiparametric Mixture Cure model.

 The Semiparametric Mixture Cure model could estimate the long-term and short-term distant metastatic-free survival based on prognostic factors.

Gastric cancer (GC) is one of the most prevalent cancers and the first cause of cancer-related deaths in Iran. These patients are diagnosed at advanced stages, which leads to poor prognosis because there is inadequate knowledge of molecular factors to provide diagnostic/prognostic biomarkers. The aim of the current study was the comprehensive analysis of enhancer RNAs (eRNAs) to determine their molecular interactions with other genes and propose a survival-related model for patients.

RNA-sequencing raw data of GC patients were downloaded from The Cancer Genome Atlas (TCGA), and differentially-expressed eRNAs and genes in tumor compared to non-tumor samples were extracted. The target genes of each eRNA were then identified based on physical distance and correlation and a regulatory network was constructed with these elements. A prognostic model for predicting the overall survival of patients was eventually established by performing univariate and multivariate Cox regression analyses, and the performance of the model was surveyed.

By performing differential expression analysis, 69 and 2606 differentially-expressed eRNAs and genes were extracted, respectively and by identifying the relationships between these elements, a regulatory network consisting of 84 nodes and 119 edges was constructed. A three-components' survival-related model subsequently was established which had the ability to predict patients' outcomes.

Based on the results, the 3-component constructed model, including an eRNA and two other genes, can be considered as a possible prognostic tool; however, further research is needed to clinically implement it.

The lung cancer is the third cause of death and also one of the five common cancers in Iran. In this study, the efficiency of semi-parametric Cox models and Weibull parametric models in order to evaluate the effective factors of survival time of patients with lung cancer were investigated.
This study was a prospective-cohort study in which the total number of 228 patients with lung cancer followed up from 1991-2007 and necessary information such as age at the time of diagnosis, gender, place of residence, education, residence status smoking, family history of cancer, province were collected from Babol cancer registry center. Then, these added to Cox and Weibull models as demographic factors. Akaike information criterion (AIC) was used to compare the efficiency of competing models.
In this study, 75% of cases were men and only 8% of patients survived until the end of the study. The 1, 3, and 5- year survival rates were 13, 8 and 8% respectively. Among the factors studied in the Weibull model, the effect of smoking on survival time was significant (p0.05). However, AIC suggested higher efficiency for parametric Weibull model.
In spite of the importance of the Cox model is as a more common method by researchers, this study showed that the Weibull model is more efficient in survival data analysis. According to the results of this study smoking prevention is necessary to increase the longevity of patients with lung cancer.

Introduction & Breast cancer is known as the most common site-specific cancer as well as it is referred to as the second cause of mortality for women. Literature indicates that the most important prognostic factors are addressed by lymph node involvement, the activity of estrogen and progesterone receptors, tumor size, tumor grade, histologic type, and epidermal growth factor. Published researches address that these prognostic factors and the relationship between them are known as important indicators to assess the effectiveness of diagnosis of a treatment method and a treatment method influence of the breast cancer. However the published researches do not address analyzing the impact of the receptor status on lymph node involvement and the impact of the relationship between the prognostic factors on survival of patients with breast cancer. This study attempts to not only investigating the impact of the receptors on lymph node involvement, but also the survival of breast cancer patients considering simultaneously the receptor status and lymph node involvement prognostics, is analyzed.
In this study, 1822 patients diagnosed with breast cancer have been considered. These patients after treating in the period of 2007 till mid 2016 were cared under adjuvant therapy such as hormone therapy, chemotherapy and radiotherapy. In this study, receptor groups are defined in 8 categories based on different combinations, as well as the number of lymph nodes involvement are categorized in 4 branches (i.e. no lymph node involvement, between 1-3 lymph nodes involvement, between 4-9 lymph nodes involvement, and more than 9 lymph nodes involvement). Receptor statuses of these patients were analyzed using the logistic regression approach. In this research, the statistical software R has been used for data analysis.
The age mean (± Standard deviation) of the patients were addressed by 50.45 (±11.47) years. Lymph node involvement was recognized for more than half of the patients (933 people i.e. 51.20%). The frequency of each 8 receptor groups were obtained as (263 individuals) 14.43%, (222 individuals) 12.18%, (762 individuals) 41.82%, (138 individuals) 7.57%, (40 individuals) 2.19%, (70 individuals) 3.84%, (11 individuals) 0.54%, (22 individuals) 1.20%, respectively. Group 3 of the receptor status (i.e. simultaneous prognostics of positive estrogen, positive progesterone, and negative HER2), addressed the highest percentage of (762 individuals) 41.82%, and Group 7 (i.e. simultaneous prognostics of negative estrogen, positive progesterone, and HER2-positive) addressed the lowest percentage of (11 individuals) 0.54% among the other groups. P-value is addressed by 0.01 and 0.05 for estrogen and HER2, respectively, when the impact of receptors on lymph nodes has been analyzed.
This study indicated that: 1) there exist relationship between estrogen receptors and Her2 with lymph node involvement, so that these receptors impact on involving the lymph nodes. 2) In the case that the prognostics of positive estrogen, positive progesterone, and negative Her2 occur simultaneously, the receptor status addresses the best condition of the survival for a patient with breast cancer compared to other groups.

Restenosis after stenting constitutes is one of the major concerns among cardiologists’ experts. This may lead to repetition of angioplasty, coronary artery bypass graft surgery, myocardial infarction, and even death. The present study, by using Cox's regression model aims at determine the demographic and clinical factors affecting time to incidence of cardiovascular restenosis in patients’ undergone angioplasty in Zanjan, Iran.
In this descriptive longitudinal study, 421 patients who visited Zanjan's Ayatollah Mousavi hospital for drug eluting stent implantation between April 2009 and June 2011 are scrutinized with respect to the time of the incidence of restenosis. Next, demographic variables and patient's clinical records are entered into “Data Gathering Form of Patients’ Undergone Angioplasty”. Data is analyzed using Cox's regression and SPSS 21.
The mean time of restenosis incidence is 736 days and the rate of restenosis incidence is 30.4%. Hazard ratio of restenosis in unemployed patients is 1.39 times, in drug addicted patients 1.75 times, in patients with a history of hyperlipidemia 1.57 times, and in patients with chronic kidney disease is 1.58 times more than other patients (P This study indicated that the most important factors leading to the incidence of restenosis are unemployment, drug addiction and abuse, suffering from hyperlipidemia, and chronic kidney diseases. Accordingly, it is highly recommended to prevent patients from above-mentioned risk factors and plan a periodic and continuous follow-up for patients with any of these factors.

Frailty models were utilized in survival models to take into account created heterogeneity and dependence between experimental units. Parametric and semi-parametric methods are considered for estimation of parameters in frailty models. In parametric frailty models, to frailty and baseline hazard, a parametric distribution is assumed, while the distribution for baseline hazard function is not considered in semi-parametric methods. Besides, the parameters using EM algorithm or penalized likelihood are estimated in the mentioned methods. The purpose of this study is the comparison of parametric and semi-parametric methods in frailty models.
Two hundred thirteen of the warfare victims that treated with dental implants during 2000 to 2010 were enrolled in this study. In order to investigation effective factors in survival of the dental implants placement, frailty models are fitted. Parameters are estimated using the three methods, parametric approach, semi- parametric using EM algorithm and Semi parametric using penalized likelihood. Statistical analysis was carried out using Frailtypack package in R software version 3.3.1.
Estimation of the semi parametric using penalized likelihood approach is contained a smaller magnitude of AIC as compared to parametric and semi-parametric approach of EM algorithm. Smoking and implant length are significant factors on survival implants (p In survival analysis using frailty models made more valid results in order to consider depending between survival times of experimental units. The penalized likelihood method has a better fit in order to estimate parameters in the frailty models.

todays, kidney failure is one of the costly problems of human society and use of renal replacement therapy is increasing in the world and Iran. Survival analysis is one of the fields in medical prognosis and data mining is a process of discovering unknown relationship and is a useful pattern from data and is known as a highly efficient method in survival analysis. Conclusively, the purpose of this study is predicting the survival of the kidney transplant patient's according to variables before kidney transplant.
In order to identify important factors for predicting survival in kidney transplant, informative requirements assessment was done by using self-designed questionnaire. Then, obtained information from the analysis of questionnaire was reviewed and data from 513 medical record of kidney patient in Sina Urology Research Center was extracted. Ultimately, by applying CRISP methodology, data mining was done by IBM SPSS Modeler 14.2 and C.5 algorithm.
In this study, BMI, ESRD and dialysis time were evaluated as the most effective factors in survival kidney transplant and extracted rules from the model can be used for predicting the survival of the transplanted kidney before the surgery. Accuracy rate of this model was estimated at 96.77%.
The high accuracy rate of C5.0 model shows the power of it in survival prediction. Furthermore, the most effective kidney transplant survival factors were identified and kidney transplanted survival of a new patient with distinctive features, can be predicted

Introduction & Survival analysis using statistical methods is known as the main method that is used by medical researchers to determine prognosis indicators. The objective is to identify and analyze different models of breast cancer survival introduced in literature.
In this study¡ a variety of models introduced in literature from 1984 to 2016 is considered. Furthermore the survival rates of studied patients addressed by different sources are investigated in this research. This investigation considers 40 breast cancer models by analyzing more than 70 scientific references. This research includes analysis of parametric¡ semi-parametric and nonparametric survival model types introduced for breast cancer patients.
This comprehensive review of 40 different models of breast cancer survival addresses that the most important factors of the survival are age¡ lymph node and its number¡ the disease stage¡ malignity grade¡ tumor size and hormone therapy (where it depends on the status of estrogen and progesterone receptors). This comprehensive study addresses that impacts of survival analysis methods on treatment protocols have not been reported by researchers.
This comprehensive research indicates that the literature is not capable of providing the opportunity for one to select a comprehensive model among the models introduced by researchers. This research addresses that Cox approach is referred to as the best model approached by researchers to analyze breast cancer survival.

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

Blood cancer (leukemia) is the cancer of blood tissue and Acute Myeloid Leukemia (AML) is the third most prevalent type in Iran. Survival time after diagnosis and its risk factors are one of the most important indicators for assessing the treatment. Data for this prospective study belong to Valiasr 2 (Imam Khomeini Hospital) AML patients during 2008-2013. A total of 85 patients were enrolled and death time was considered as failure event for them. Kaplan-Meier method and Cox proportional hazard model were used to identify the risk factors of patient’s survival. The SPSS v.21 and SAS v. 9.2 were used for analysis of data and α=0.05 was considered as significant level. Of 85 patients, 46 (54.1%) were male. The mean age at diagnosis time was 41.38 yr., and median survival time was 17.27, and 53 patients died. Age at diagnosis, marital status, smoking, bone marrow transplants, disease history, region, lodging and resistance to treatment, and some laboratory factors (such as the number of white blood cells and red blood cells, mean corpuscular hemoglobin, sodium, potassium and calcium) were significant on survival time of patients. Because of the importance of leukemia, especially the role of AML type in mortality, an emphasis on age at diagnosis, marital status, smoking, bone marrow transplant, disease history, region and resistance to treatment and some laboratory factors are very important in the prognosis of death in patients.

Thalasemia Major is one of the most common anemia diseases that can be fatal if not promptly diagnosed. The survival analysis of these patients can be an appropriate strategy in determining risk factors for death in these patients. The purpose of this study was to choose the best model to determine the risk factors for death in patients with the thalasemia major using common methods in the survival analysis.

The data of this retrospective cohort study, with 296 patients with thalassemia major, was collected in 2004- 2013 in Zafar Clinic in Tehran. Akaicke information Criterion was used for comparison of the models and the choice 351of the best model. Data analysis was carried out with R3.0.2 software at the significant level of 0.1.

The values of the Akaicke information criterion (AIC) for the parametric weibull, frailty weibull, log-normal, log-logistic, Gompertz, gamma and the semiparametric Cox were computed and found to be 27.56, 29.56, 18.73, 23.39, 26.26, 68.10, 24.73, respectively. The mean survival time for men and women were 40.2 and 39.7 years, respectively. The Log-normal model showed that age, age at the first desferal injection, onset of blood injection, the patient's birthplace, mother's education variables were significantly correlated with patient survival.

According to the values of AIC, the parametric log-normal model was chosen and suggested as the best model.

Identification of the factors influencing short term and long term survival for patients is one of the important concerns in medical society. With determining the risk factors and also health factors of a disease, valuable information can be obtained. In some cases, such as high rate of censoring, the use of standard survival models for analysis may cause to loss in some of information. The purpose of this study was to apply the cure models in identifying the risk and health factors of patients’ thalassemia major.

The data of this historical cohort study with 296 patients with thalassemia major, were gathered during 1994- 2013 in Zafar Clinic in Tehran. Cure models were used for survival analysis of these patients and determination of its risk factors. Data were analyzed using Stata v.12 and R3.0.2 software.

Mean (±SD) of patients’ age was 29.11 ± 0.47year. Of the patients, 51.7% were women.. The minimum value of Akaike information criterion was for cure non mixture log – normal model with logit link function.

According to the results, the cure non mixture log – normal models is chosen as the best model for identification the risk factors of thalassemia major disease.

In analyzing survival data using conventional methods of classical statistics requires some basic assumptions for data. Artificial neural networks as a modern modeling method can be used in situations where classic models have restricted application because their assumptions are not met. This study is compared survival of patients with breast cancer using artificial neural network and Cox regression models. This historical cohort study, include data from 161 patients with breast cancer in Ardabil province in the years 2002-2007 were diagnosed as having cancer. 68.9% of data dividing as training data set and 31.1% of data dividing as validation data set. Artificial neural networks and Cox regression models are fitted to data. Predictive accuracy and area under ROC used to compare models. Between neural network models, models with SCG, OSS and LM learning algorithms with predictive accuracy of 94, 90 and 78 percent for validation data, had the highest efficiency respectively. Areas under ROC for these models are 0.991, 0.972 and 0.837 respectively and 0.869 for Cox regression model. This study shaw that if suitable architecture and algorithms are selected for artificial neural network model, this model will be more efficient than the Cox regression model to predict the survival situation of patients with breast cancer.

Background and

Gastric cancer is one of the most common cancers in the world and Mazandaran province is known as one of the areas with high incidence of gastric cancer in Iran. This study examined the survival rates and factors affecting survival of patients with gastric cancer treated in Touba Clinic of Sari.

In this retrospective study the information of 430 patients with gastric cancer were studied. They were followed up for a 5-year period from November 2007 until December 2012. The overall survival rates were determined using nonparametric method of Kaplan - Meier and univariate analysis, and log rank test was used to determine the factors affecting the survival rates of the patients.

Among the patients 68.8% were male and 31.2% were female. The mean age of male patients was 65.98 years and in female it was 61.12 years. The mean and median survival rates of these patients were 27.82 months and 19 months, respectively. The survival rate of the studied population in one, two, three, four and five years were 0.64, 0.44, 0.34, 0.28 and 0.19, respectively. Univariate analysis identified age at diagnosis (p= 0.001), degree of differentiation of tumor (p= 0.031), metastasis of disease (p= 0.000), tumor stage (p= 0.000), type of treatment for the patient (p= 0.000), histopathology type (p= 0.013), and location (p= 0.009) as effective diagnostic factors.

Five-year survival rate of patients with gastric cancer is low in Sari. Late referred and delayed diagnosis could be the reasons, therefore, early detection and screening methods are of great importance in improving the survival rate in these patients

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. 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. 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). Cure models have the ability to analyze dialysis patient's 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.

Introduction & Myocardial infarction (MI) is one of the most important causes of mortality in human communities today. MI can disable patients, and moreover it imposes financial burden on society. Therefore, regarding to the effect of some variables in the trend of the disease, we tried to determine a 5 year survival rate for patients with MI and assess the effect of some factors on it in Bushehr port. In this retrospective cohort study, 197 patients suffering from myocardial infarction for the first time and admitted in Bushehr hospitals during "1997-2001" were evaluated. The patients were followed up for 5 years and the relationship between their survival rate and factors such as patient's age at the time of MI, their sex and what time MI happened was evaluated. The mean 5 year survival time for the patients after the first myocardial infarction was approximately 53.7 months (53.5 for men, 54.4 for women; p>0.05). In this study, only age of the patients had a significant relationship with their survival rate (P=0.018). The study showed that mortality rate in hospitalized patients in the first 28 days, one year and 5 years after myocardial infarction were 1%, 8.6% and 13.7% respectively. MI in Bushehr port mostly occurred in fall and summer respectively. The study indicated that MI mostly happened in the early morning In this study, the 5 year survival rate of patients (86.3%) was higher than that of patients in other studies.

The incidence of survival analysis and hazard rates in 55 patients with the pemphigus have been determined for a period of 12 years from 1989- 2000. The effect of pemphigus type, sex, age and the method of treatment were evaluated separately. For this equation ht(x)=ho(t) exp {?? i Xi} was chosen in which ho is the baseline hazard in zero time, ht is hazard happened later (in time t),? is regression coefficient and X can be age, sex, pemphigus type and method of treatment. Results of this study revealed that the upper age groups had relatively smaller survival chance than those of lower age groups, and women less than men. The first 8 years after pemphigus diagnosis, patients with vulgaris and vogtan type as compared with foliaceae and etythmatous had also less survival chance, so was combined therapy compared with single therapy (Corticosteroides). Other parameters like job type and place of living had no apparent effect.