cox regression

In this study, data were collected from the Eastern Democratic Republic of Congo and analyzed by Cox regression model. In addition, hazard functions and survival outcomes in COVID-19 patients were also analyzed.

One million simulated data on hospitalized patients’ characteristics with positive SARS-CoV-2 infection were collected from the Humanitarian Data Exchange Source in the Eastern Democratic Republic of Congo from December 2020 to June 2021. Several statistical techniques were developed in this study for data analysis, including Kaplan-Meier curves, log-rank test, Schoenfeld residual diagnostics, and likelihood ratio test.

This study finding showed that there was a 4.5% increase in the expected hazard per unit year increase in age. In addition, the risk of death was higher in males than in females, and patients with no signs of anorexia, ageusia, or anosmia, no history of diabetes or tuberculosis, normal pulse rates, and no hypoxemia had a greater survival rate than those with such health conditions.

This study finding revealed that covariates such as age, gender, anorexia, ageusia, anosmia, diabetes, and tuberculosis were expressively connected with higher mortality rates. In addition, hypoxemia and high pulse rate were associated with higher death rates; however, anti-inflammatory and anticoagulant agents were shown to reduce mortality rates, and multivitamin or vitamin C had a substantial impact on patient survival.

Remdesivir, an antiviral medication, became an early promising therapeutic candidate for coronavirus disease 2019 (COVID-19) due to its ability to inhibit the virus in vitro. Current evidence about remdesivir treatment has been very controversial, so we aim to evaluate remdesivir to improve our knowledge about COVID-19 management and its long-term effects. In this retrospective cohort study using registered data derived from the Sina Hospital COVID-19 Registry with a 9-month follow-up, we enrolled patients receiving remdesivir and then matched a "control group" which did not receive remdesivir based on age, gender, and severity using propensity score matching. We used multivariant Cox regression to evaluate the remdesivir effect on patients' 9-month and in-hospital survival. We enrolled 227 patients, 116 in remdesivir and 111 in the control group. 213(93.8%) patients developed the severe disease, 88(38.8%) died during the 9-month follow-up, and 84(37.0%) died during hospitalization. In multivariate analysis, remdesivir did not affect the 9-month all-cause mortality and in-hospital mortality. Remdesivir was associated with increased in-hospital survival only in severe patients with diabetes (HR: 0.32; 95% CI: 0.14-0.75; P:0.008), and there was a trend for better 9-month survival in severe patients with diabetes (HR: 0.47; 95% CI: 0.20-1.09; P:0.080). We concluded that remdesivir treatment did not increase the 9-month survival rate either in patients with COVID-19 or patients with severe disease and underlying diseases. On the other hand, we found that remdesivir treatment could increase in-hospital survival only in patients with severe COVID-19 and a history of diabetes mellitus.

The study aimed to estimate the overall and disease-free survival rates of breast cancer patients and the factors affecting these rates.

In this retrospective study, data were obtained from 686 patients diagnosed with breast cancer in Sivas Cumhuriyet University Faculty of Medicine Research and Application Hospital Oncology Center between 1988 and 2014. Total population sampling method was used. The survival rates at certain periods were determined by creating a Life Table. By using the Kaplan-Meier Analysis, the mean survival times and rates were determined, and whether the variables had an impact on survival was examined. By applying Cox regression analysis, the effect of prognostic factors that are significant on the survival time of breast cancer patients was examined.

Overall mean survival time was found as 208.4±11.8 months. According to Kaplan-Meier analysis, 1, 5, 10 and 20-years overall survival rates were 96.6 ± 0.07%, 82.3 ± 1.7%, 64.4 ± 3.4% and 49%± 7.4%, respectively. According to Cox regression analysis results, variables that influence overall survival time were found as disease stage, multicentricity status, ECOG (performance status), presence of diabetes, CA15-3 value, neutrophil/lymphocyte ratio. Moreover, variables that had an impact on the disease-free survival time were found as tumor grade, multicentricity, and ECOG.

Many factors other than disease can prolong survival or accelerate death. Considering the findings of this study may be useful in planning the treatment of breast cancer patients have positive affect on overall survival rates.

Coronary artery disease is the most common cause of death worldwide as well as in Iran. The present study was designed to predict short and long-term survival rates after the first episode of myocardial infarction (MI).

The current research is a retrospective cohort study. The data were collected from the Myocardial Infarction Registry of Iran in a 12-month period leading to March 20, 2014. The variables analyzed included smoking status, past medical history of chronic heart disease, hypertension, diabetes, hyperlipidemia, signs and symptoms during an attack, post-MI complications during hospitalization, the occurrence of arrhythmias, the location of MI, and the place of residence. Survival rates and predictive factors were estimated by the Kaplan–Meier method, the log-rank test, and the Cox model.

Totally, 21 181 patients with the first MI were studied. There were 15 328 men (72.4%), and the mean age of the study population was 62.10±13.42 years. During a 1-year period following MI, 2479 patients (11.7%) died. Overall, the survival rates at 28 days, 6 months, and 1 year were estimated to be 0.95 (95% CI: 0.95 to 0.96), 0.90 (95% CI: 0.90 to 0.91), and 0.88 (95% CI: 0.88 to 0.89). After the confounding factors were controlled, history of chronic heart disease (p<0.001), hypertension (p<0.001), and diabetes (p<0.001) had a significant relationship with an increased risk of death and history of hyperlipidemia (p<0.001) and inferior wall MI (p<0.001) had a significant relationship with a decreased risk of death.

The results of this study provide evidence for health policy-makers and physicians on the link between MI and its predictive factors.

Ovarian cancer is one of the most deadly women's gynecological malignancies in the world, and despite the low prevalence, it accounts for about 5% of all cancer deaths in women. Survival analysis is a regression relationship between a set of variables with a specific outcome, which is considered disease survival or recurrence in medical studies. The aim of this study is to determine the important factors in the first recurrence of patients with epithelial ovarian cancer with two statistics methods. In this study, we review medical records of patients with epithelial ovarian cancer who referred to the oncology and radiotherapy department of Imam Hossein Hospital of Tehran from the beginning of 2007 to the end of 2018. Univariate and multivariate Cox regression, as well as the parametric Weibull method, were used to investigate the factors affecting patients' first recurrence. We perform all calculations with Stata Ver14. Of the 141 patients, 58 patients (41%) had a first recurrence during our follow-up. The mean time to the first recurrence was 24.88 months. Univariate Cox regression and univariate Weibull analysis showed that metastatic tumor and tumor stage had highly significant effects in the first recurrence of epithelial ovarian cancer. In multivariate Cox and multivariate Weibull analysis, the metastatic tumor had a significant effect in the first recurrence of epithelial ovarian cancer. One of the causes of ovarian cancer recurrence may be diagnosis happened at late stages. Therefore, screening programs are needed to reduce illness and death from ovarian cancer.

Colorectal cancer (CRC) is the third prevalent cancer worldwide, and it includes 10% of all cancer mortality. In Iran, men and women have the third and the fourth incidence rate of CRC, respectively. Survival analysis methods deal with data that measure the time until an event occurs. Artificial neural networks (ANN) and Cox regression are methods for survival analysis.

The current study was designated to determine related factors to CRC patients’ survival using ANN and Cox regression.

In this historical cohort, information of patients who were diagnosed with CRC in Omid Hospital of Mashhad was collected. A total of 157 subjects were investigated from 2006 to 2011 and were followed up until 2016. In ANN, data were divided into two groups of training and testing, and the best neural network architecture was determined based on the area under the ROC curve (AUC). Cox regression model was also fitted and the accuracy of these two models in survival prediction was compared by AUC.

The mean and standard deviation of age was 56.4 ± 14.6 years. The three-, five- and seven-year survival rates of patients were 0.67, 0.62, and 0.58, respectively. Using test dataset, the area under curve was estimated 0.759 for the chosen model in ANN and 0.544 for Cox regression model.

In this study, ANN is an appropriate approach for predicting CRC patients’ survival which was superior to Cox regression. Thus, it is recommended for predicting and also determining the influence of risk factors on patients’ survival.