جستجوی مقالات مرتبط با کلیدواژه « Cox Proportional Hazards Model » در نشریات گروه « پزشکی »

The first-birth interval after a marriage indicates the reproduction behavior of women and influences the population's birth rates and size. The present study assesses predictors of the interval between marriage and first childbirth in India.

The study employed the Cox proportional hazard model and Kaplan Meier Survival plot based on the data collected from 79,787 ever-married women in the 15-49 age group from the National Family Health Survey 2019-2021.

The median age of marriage to the first birth interval was 23 months in India. The older marriage cohort had longer birth intervals than the younger. The hazard ratios (HR) showed that the risk of first birth after marriage was much higher among women with higher education (HR= 2.05, 95% confidence interval (CI) = 1.98-2.11) than women without education. Women in urban areas (HR=1.22, 95% CI = 1.20-1.25) had a higher risk of first birth after marriage earlier than women from rural areas. Women from North-east (HR=1.14, 95%CI=1.10-1.18) and South (HR=1.15, 95%CI=1.12-1.19) had a higher risk of having their first birth earlier after marriage than women in the North region. The women who married within 18-24 years of age had a 69 percent higher likelihood of first birth interval than those women who were married below the age of 18. The risk of first birth after marriage increased as women delayed marriage up to age 25 years and more (HR=3.18, 95% CI=3.02-3.35) than others.

The timing of first birth was associated with the age at the first marital union, women's educational attainment, place of residence, region, economic status, exposure to mass media, contraception use, and history of pregnancy termination.

Myocardial infarction (MI), is considered as an important cause of death and disability. About three - quarters of the deaths caused by heart diseases occur in countries with low or middle economic levels. Evaluation of the effective risk factors and medications and the overlap of their compounds on the long - term survival of patients with myocardial infarction

In this retrospective cohort study 21181 patients, hospitalized in coronary care units (CCU) hospitals of Iran enrolled from the MI registry system for the period of 20 March 2013 to 20 March 2014.
Participants were followed up to February 2020 for any cardiovascular disease (CVD) mortality. To evaluate survival rate, difference between groups and to assess factors related to MI death, Kaplan–Meier, Log rank test and Cox Proportional-Hazards Model were used, respectively.

The mean age of patients was 62.10± 13.41. 72.37 % of the patients were men. The rate of survival in the time period in females was less than males. For those patients without the history of coronary diseases, hypertension, and diabetes but with hyperlipidemia and smoking, the 1, 3, 5, and 7-year survival rates were better compared to other patients. The 1, 3, 5, and 7-year survival rates of those in group 6 (Beta blocking agent) were higher compared to other medication groups.

Controlling risk factors can reduce the mortality rate, and the recommendations of doctors and adherence treatment plays an important role in the long - term survival of Myocardial infarction patients.

Birth spacing, especially the first birth interval (FBI), is a suitable index to investigate the delayed fertilitythat results in a low fertility pattern. Non-parametric familiar alternatives to the Cox proportional hazard regression(CPH) model include survival trees that can automatically discover certain types of covariate interactions according tothe survival length. The aim of this research is to study FBI influential factors by applying survival trees. In this cross-sectional study, 610 married women (aged 15-49 years), were selected fromdifferent regions of Tehran, Iran in the Winter and Spring of 2017. Classification and regression trees (CART) for theFBI survival tree were fitted by taking into consideration the predictors of each woman’s age, age at first marriage,educational level, partner’s educational level, activity, region, house ownership, kinship, partner’s race, marriage timeattitude, and expenditure using R packages. Since the PH assumption of the CPH model was not confirmed for the covariates of age at first marriage (P=0.001),kinship (P=0.000), partner’s race (P=0.001), and marriage time attitude (P=0.042), the results of this model were not valid.Thus, a CART survival tree was fitted. The validity of the fitted model in assessing FBI was confirmed by the significantresult of the log rank test (P<0.01) for the terminal nodes and the value of the separation measure, which was greater than1. The fitted tree had 13 terminal nodes and the most vital FBI predictor was women’s age. The longest FBI belonged toeducated and employed women, ages 30-37 years. Analysing patterns of birth spacing by selecting the appropriate statistical method provides important informationfor health policymakers. In order to formulate appropriate demographic policies, it is essential to take into considerationage, educational level and job status of the women, all of which have essential roles on their decision to have children.

The present study was performed to investigate the timing of the first deciduous tooth eruption in infants and its relationship with environmental and nutritional factors such as weight, height, and head circumstance at birth, type of childbirth, duration of breastfeeding, and initiation time of semi-solid food.

This study is a prospective study conducted on newborn infants during 2008-2009 years. For these infants, variables such as parental education level, parental ages, type of childbirth, infant's breastfeeding duration, initiation time of semi-solid food, weight, height, and head circumstance at birth, and then the researchers entered the data into the statistical software STATA 12 and analyzed the data by COX regression model.

The mean age of the infants receiving semi-solid food was 0.033 ± 5.99 months, and the mean height of the infants at birth was 0.078 ± 50.26 cm. The mean weight of the newborns at birth was 0.016 ± 3.37 kg. Finally, the mean head circumference of the newborns at birth was 0.062 ± 35.02 cm. By increasing the maternal childbearing age, the eruption time of the first primary tooth increases in the newborns. Furthermore, increasing the age of receiving semi-solid food in infants causes an increase in the eruption time of primary tooth in newborns.

The study uncovered that higher maternal childbearing age, increased duration of breastfeeding, increased age of infants in initiation time of receiving semi-solid food were significantly associated with delayed eruption of the first primary tooth in infants. However, infant's high birth weight had a significant relationship with the earlier eruption of the first primary tooth.

In recent years, sexually transmitted diseases such as AIDS have become an epidemic and are growing rapidly. Given the importance of controlling the disease in recent years, the awareness of the most important risk factors associated with patient survival is important. Therefore, this study aimed to determine the most important factors affecting the survival of HIV patients using the random survival forest (RSF) method.

In this retrospective study, medical records of 769 HIV patients in Hamadan Health Center from 1997 to 2017 were used to determine the most important factors in patient survival using Cox proportional hazards model and RSF method. The Brier score and C-index were applied to compare the Cox model and RSF method.

Based on the results, 662 (86.1%) patients were male. The mean ± SD diagnosis age was 33.83 ± 9.63 years. Using Cox model, variables such as injection history, co-injection history, tuberculosis (TB) status, the first CD4 cell count, and the time of disease diagnosis until TB were determined to be variables affecting the survival of patients. According to the hazard ratio (HR), the risk of death for those with a history of injections was 12.328 times greater than that of noninjectors, and for those with TB, it was 13.565 times greater than that of non-TB patients. An increase in CD4 cell counts was associated with a decline in the risk of mortality. Based on the log-rank model, the variables such as the time until diagnosis of TB, the first CD4 cell count, ART, and history of co-injection had the highest impact on predicting the survival of HIV+ patients, respectively.

In case of the presence of many risk factors and the relationship between risk factors, the use of RSF offers a better performance in determining the influential survival factors as compared to Cox model which has limiting presumptions. 

Application of statistical machine learning methods such as ensemble based approaches in survival analysis has been received considerable interest over the past decades in time-to-event data sets. One of these practical methods is survival forests which have been developed in a variety of contexts due to their high precision, non-parametric and non-linear nature. This article aims to evaluate the performance of survival forests by comparing them with Cox-proportional hazards (CPH) model in studying first birth interval (FBI).

A cross sectional study in 2017 was conducted by the stratified random sampling and a structured questionnaire to gather the information of 610, 15-49-year-old married women in Tehran. Considering some influential covariates on FBI, random survival forest (RSF) and conditional inference forest (CIF) were constructed by bootstrap sampling method (1000 trees) using R-language packages. Then, the best model is used to identify important predictors of FBI by variable importance (VIMP) and minimal depth measures.

According to prediction accuracy results by out-of-bag (OOB) C-index and integrated Brier score (IBS), RSF outperforms CPH and CIF in analyzing FBI (C-index of 0.754 for RSF vs 0.688 for CIF and 0.524 for CPH and IBS of 0.076 for RSF vs 0.086 for CIF and 0.107 for CPH). Woman’s age was the most important predictor on FBI.

Applying suitable method in analyzing FBI assures the results which be used for making policies to overcome decrement in total fertility rate.

Gastric cancer is the fourth most common form of cancer and the second most common cause of death in the world. It is also one of the most common cancers leading to mortality in Iran. Therefore, this study aimed to determine the survival rate of patients with gastric cancer and its affecting factors in the south of Iran (Hormozgan province). 

In this study, all patients with gastric cancer (119 patients) that were diagnosed and registered during 2008 to 2013 in Hormozgan province, were studied. All patients were followed to the end of 2015. Kaplan-Meier method and Cox proportional hazards model were used to draw survival curves and to determine the effective factors on the survival rate of surveyed patients. Moreover, Log-rank test was used to evaluate whether or not survival curves for different groups are statistically equivalent (p<0.05).

The mean age of the study population was 58.9±14.91, and most of them were men (72.3% (86 persons)). After diagnosis, the survival rates for 1, 2, 3, 4, and 5 years were 62.2%, 49.4%, 43.7%, 39.7%, and 38% respectively. Survival in men were lower than women, but according to log-rank test this difference was not statistically significant (p=0.325). Also patients with advanced stage cancer had significantly lower survival in comparison to individuals with early stage disease (p<0.001). Based on multiple Cox proportional hazards model, job status of the patients and stage of cancer   were effective factors on patients’ survival.

Based on the findings of the present study, the survival rate was decreased over time after diagnosis. Stage of a cancer at the time of diagnosis is the most important factor affecting the survival of surveyed patients. This shows that there is a crucial need to diagnos the gastric cancer in early stages.

Cox proportional hazard model is the most common method for analyzing the effects of several variables on survival time. However, under certain circumstances, parametric models give more precise estimates to analyze survival data than Cox. The purpose of this study was to investigate the comparative performance of Cox and parametric models in a survival analysis of factors affecting the event time of neuropathy in patients with type 2 diabetes.
This study included 371 patients with type 2 diabetes without neuropathy who were registered at Fereydunshahr diabetes clinic. Subjects were followed up for the development of neuropathy between 2006 to March 2016. To investigate the factors influencing the event time of neuropathy, significant variables in univariate model (P Using Kaplan–Meier, survival time of neuropathy was computed 76.6 ± 5 months after initial diagnosis of diabetes. After multivariate analysis of Cox and parametric models, ethnicity, high?density lipoprotein and family history of diabetes were identified as predictors of event time of neuropathy (P According to AIC, “log?normal” model with the lowest Akaike’s was the best?fitted model among Cox and parametric models. According to the results of comparison of survival receiver operating haracteristics curves, log?normal model was considered as the most efficient and fitted model.

Recently, reports have shown that gastric cancer has high abundance in Iran and is at the second level in men, and fourth in total. This study aimed to determine the 5-year survival of gastric cancer patients and to investigate factors affecting the performance, based on TNM-7 staging system. In this study, we investigated 760 patients with gastric cancer since the beginning of 1993 to the end of 2006 in the Iran Cancer Institute who underwent surgery. Survival of these patients was determined after surgery, and the effects of demographic characteristics such as age (during operation), sex, and information on diseases such as cancer site, pathologic type, stage of disease progress (Stage), metastasis and sites of metastases were evaluated. The 5 -year survival probability of patients was 28 %, and median survival time was 25.69 months. Univariate tests showed that sex, cancer site, and pathologic type have no significant effects on patient’s survival. But the probability of 5-year survival significantly decreases with increasing age, and as it is expected, those with metastases were significantly less likely to have 5-year survival, and disease stage was significantly effective on patients’ life (P

Transplantation is the only treatment for patients with liver failure. Since the therapy imposes high expenses to the patients and community, identification of effective factors on survival of such patients after transplantation is valuable.. The current study attempted to model the survival of patients (two years old and above) after liver transplantation using neural network and Cox Proportional Hazards (Cox PH) regression models. The event is defined as death due to complications of liver transplantation..Patients and In a historical cohort study, the clinical findings of 1168 patients who underwent liver transplant surgery (from March 2008 to march 2013) at Shiraz Namazee Hospital Organ Transplantation Center, Shiraz, Southern Iran, were used. To model the one to five years survival of such patients, Cox PH regression model accompanied by three layers feed forward artificial neural network (ANN) method were applied on data separately and their prediction accuracy was compared using the area under the receiver operating characteristic curve (ROC). Furthermore, Kaplan-Meier method was used to estimate the survival probabilities in different years.. The estimated survival probability of one to five years for the patients were 91%, 89%, 85%, 84%, and 83%, respectively. The areas under the ROC were 86.4% and 80.7% for ANN and Cox PH models, respectively. In addition, the accuracy of prediction rate for ANN and Cox PH methods was equally 92.73%.. The present study detected more accurate results for ANN method compared to those of Cox PH model to analyze the survival of patients with liver transplantation. Furthermore, the order of effective factors in patients’ survival after transplantation was clinically more acceptable. The large dataset with a few missing data was the advantage of this study, the fact which makes the results more reliable..