جستجوی مقالات مرتبط با کلیدواژه "parametric model" در نشریات گروه "پزشکی"

Head and neck cancer is one of the most important cancers with low survival. This study was designed to evaluate the one-year survival of patients with head and neck cancer and related demographic factors.

The present study was a cross-sectional study that reviewed the records of the patients with head and neck cancer (193 patients) in 2007-2013. In this study, Kaplan-Meyer method and Cox multiple regression were used to analyze the data. In this study, the information obtained by Excel and Stata 16 software was analyzed.

The results showed 117 (60.6%) patients were male and 76 (39.4%) were female. The mean age of the patients at the time of diagnosis was 62.51± 7.76. Three, six and 12-month survivals were 66.3%, 40.5% and 11.1%, respectively. The results showed that only the age of the patients had a significant relationship with the survival of patients up to one year (HR=65.13, CI= 4.52-41.19).

One-year survival of patients with head and neck cancer in Yazd City is low and the elderly usually have a lower survival than young people.

Colorectal cancer is the most common cancer of digestive system in Iran. The incidence of this cancer has increased in recent years.The aim of this study was to evaluate the survival rate and to define the prognostic factors in Iranian colorectal cancer patients using competing risk model.
Data were recorded from 1060 patients with colorectal cancer who registered in Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences (Tehran, Iran) from 2004 to 2015 in a retrospective study. Analysis was performed using competing risks model and based on the Weibull distribution. Software used for data analysis was R and significance level was regarded as 0.05.
The result indicated that at the end of follow-up, 380 (35.8%) deaths were from colorectal cancer and 49 (4.6%) deaths were due to other diseases and 631 patients (59.5%) survived till the end of the study. The mean survival time for a patient in 1060 was 56.96±1.46 months with median equals of 45.5 months. According to competing-risks method, only age at diagnosis and body mass index has a significant effect on patient’s survival time.
Based on parametric competing risk model, just age at diagnosis and body mass index were significant prognosis of colorectal cancer survival.

Colorectal cancer is the most common gastrointestinal cancer. Investigating the factors that predict survival time for these patients is important. The purpose of this study was comparison of parametric models by estimating the prediction error and also identifying the effective factors on predicted survival time of patients with colorectal cancer. This cohort study was conducted with 600 patients who were suffered from colorectal cancer in Taleghani Hospital of Tehran between 2001 to 2005 and they were followed up for at least 5 years. For identifying the effective factors on survival time، of the patients we analyzed the data by some parametric models such as Weibull، Exponential and Log logistic and compared these models with the estimation of prediction error by apparent loss method. Among 600 patients there was 344 men (57. 3%) and 256 wemon (47. 7%). Of total، 151 patients were died that 62. 3% of them were men. Univariate analysis showed that the effect of BMI، sex، staging of tumor، tumor site were significant but in multivariate model staging of tumor and BMI were significant. By the estimation of prediction error، the best model was Log logistic. With respect to the importance of survival time prediction، we found that we can use the prediction error to compare the parametric models. In addition، because of effectiveness of tumor stages and BMI in the patients’ survival time، survival time could be increased by an on-time diagnosis and an appropriate controled diet.

Background and One of the most common methods used to estimate the effects of explanatory variables on survival time, is Cox semi parametric model. However, under certain circumstances, accelerated failure time parametric models are superior to the Cox model. The purpose of this study was to assess the efficiency of parametric and semi-parametric models in survival analysis of patients with gastric cancer. In this retrospective study, we analyzed 249 medical records of patients attending Tooba clinic affiliated to Mazandaran University of Medical Sciences. We obtained information on the final status of patients viaphone calls.Parametric methods including Weibull, loglogistic and log-normal and semi-parametric Cox model was fitted on the data to identifythe factors reducing survival time The results showed that patients with primary progress of disease, surgery as a treatment and patients without metastasis had higher survival rate than patientsin otherstages of disease and treatment (P<0.05). According to the value of Akaike Information Criterion (AIC) in univariate and multivariate analyses, lognormal model had best fitness in these data. However, parametric regression model had fitted better than Cox semiparametric regression. In this study lognormal model had highest fitness in our data. However, there were no significant differences between values of AIC of these models.According to the results, applying parametric model is suggested instead of semi parametric model if there is enough information about survival time and trend of variation.

It can be used different models such as nonparametric، semi-parametric and parametric to estimation of survival. Depending on the nature of the data، some methods may be more appropriate. Using of appropriate model result more accuracy estimation of survival and it''s affected. so the aim of this study estimation of survival and determination of its related risk factor. In this study 169 patients with colorectal cancer who had treated in Hamadan MRI Center from March 2005 to Jun 2013 were evaluated. Survival time of patients is considered from diagnosis time to death or end of study. The effect of Demographic and clinical risk factors on survival is assessed by parametric model. follow up of patient Data were analyzed using Spss16 and R statistical software. The median survival was 4/3±40 months. Univariate analysis showed that the effect of staging of tumor، tumor grade، histology type of tumor، metastasis and relapse on survival were significant but in multivariate model histology type of tumor and metastasis were significant. using of appropriate statistical model for detection of affected risk factor on patient with colorectal cancer can be prevented progression of disease and increased of survival of them. Keywords: Colorectal cancer، survival estimation، parametric model، Darol-Aitam MRI center، Hamadan province.

Cox proportional hazard regression model is the most common and standard method to study the effect of potential covariates on the survival times. However, under certain conditions such as availability of a good information about the system under study, parametric models are known to be more efficient than the Cox models. In this study, we aim to evaluate the efficiency of the semi-parametric Cox models and alternative parametric models and nonparametric Kaplan-Meier estimates in analyzing the survival times of patients suffering from acute myocardial infraction
Material & This study is a prospective-cohort research with the total number of 650 participant's experienced acute myocardial infarction. The patients followed up for one year in order to analyze their survival times. Three approaches; non-parametric, semi-parametric and parametric models, were used to model the survival times. To evaluate the relative goodness of fit of each procedures, Akaike information criterion (AIC) was used. During one year of the following up, the total number of 67 deaths were reported. Out of 460 initial patients, 48% were smoking, 33.4% suffered from hypertension, 17.7% diabetes, 15.3 Hyperlipidemia, 30% airtime, and 10.3% had block. A broad agreement was observed between parametric and Cox models. Both models associated similar covariates as significant factors. However, the non-parametric model suggested high age , non used streptokinase and block significantly increased the risk of death. However, the hazard ratio in Cox model and parametric models were similar and results of nonparametric Kaplan Meier approach were largely similar but, The AIC suggested the parametric Weibull model as the most appropriate model for modeling the survival times of patients with acute myocardial infarction.

Cox proportional hazard regression model is the most common and standard method to study the effect of potential covariates on the survival times. However, under certain conditions such as availability of a good information about the system under study, parametric models are known to be more efficient than the Cox models. In this study, we aim to evaluate the efficiency of the semi-parametric Cox models and alternative parametric models and nonparametric Kaplan-Meier estimates in analyzing the survival times of patients suffering from acute myocardial infraction This study is a prospective-cohort research with the total number of 650 participant's experienced acute myocardial infarction. The patients followed up for one year in order to analyze their survival times. Three approaches; non-parametric, semi-parametric and parametric models, were used to model the survival times. To evaluate the relative goodness of fit of each procedures, Akaike information criterion (AIC) was used. During one year of the following up, the total number of 67 deaths were reported. Out of 460 initial patients, 48% were smoking, 33.4% suffered from hypertension, 17.7% diabetes, 15.3 Hyperlipidemia, 30% airtime, and 10.3% had block. A broad agreement was observed between parametric and Cox models. Both models associated similar covariates as significant factors. However, the non-parametric model suggested high age, non used streptokinase and block significantly increased the risk of death. However, the hazard ratio in Cox model and parametric models were similar and results of nonparametric Kaplan Meier approach were largely similar but, The AIC suggested the parametric Weibull model as the most appropriate model for modeling the survival times of patients with acute myocardial infarction.