No abstract available.
Proportional Hazards Models* ; Survival Analysis*

This brief note describes the principles of survival analysis. Survival analysis is method for studying the time between entry to a study and a subsequent event and is used frequently in neurointervention studies. Kaplan-Meier estimator is nonparametric method for estimating the survival curve and log rank test is used for comparing between exposure and non-exposure groups. Proportional hazards model, a semi-parametric regression model specifically developed for censored data, is used when there are many exposure variables.
Proportional Hazards Models ; Survival Analysis*

Prevalence ; Proportional Hazards Models ; Regression Analysis

Humans ; Pneumoconiosis ; mortality ; Proportional Hazards Models

OBJECTIVE: To realize batch processing of univariate Cox regression analysis for great database by SAS marco program. METHODS: We wrote a SAS macro program, which can filter, integrate, and export P values to Excel by SAS9.2. The program was used for screening survival correlated RNA molecules of ovarian cancer. RESULTS: A SAS marco program could finish the batch processing of univariate Cox regression analysis, the selection and export of the results. CONCLUSION The SAS macro program has potential applications in reducing the workload of statistical analysis and providing a basis for batch processing of univariate Cox regression analysis.
Proportional Hazards Models ; Regression Analysis ; Software

Follow-Up Studies ; Humans ; Proportional Hazards Models ; Survival Analysis

OBJECTIVE: To investigate the gene mutation in adult patients with B-ALL and its influence on clinical prognosis. METHODS: Clinical data of 226 adult patients with B-ALL were retrospectively analyzed in the period from August 2011 to February 2018. The incidence of gene mutation in all patients were detected, and the influence of mutation gene on clinical prognosis were estimated. Cox regression model were used to evaluate the independent prognostic factors. RESULTS: 208 (92.04%) of 226 patients showed gene mutations, and the median mutation number was 2 (0-8). Among them, 54 cases (23.89%) showed 14 or more mutations. The top five mutation types of all patients were SF1, FAT1, MPL, PTPNII and N-RAS respectively. The median OS and median RFS times of 226 patients were 27.0 (5.5-84.0) months and 22.5 (0-81.0) months respectively. The OS and RFS times of Ph CONCLUSION Gene mutations are common in all adult B-ALL patients, and the clinical prognosis of patients with JAK and epigenetics-related signaling pathway mutations is worsen, while the WBC level closely relates to the clinical prognosis of the patients.
Adult ; Humans ; Mutation ; Patients ; Prognosis ; Proportional Hazards Models ; Retrospective Studies

OBJECTIVE: To compare the predictive ability of two extended Cox models in nonlinear survival data analysis. METHODS: Through Monte Carlo simulation and empirical study and with the conventional Cox Proportional Hazards model and Random Survival Forests as the reference models, we compared restricted cubic spline Cox model (Cox_RCS) and DeepSurv neural network Cox model (Cox_DNN) for their prediction ability in nonlinear survival data analysis. Concordance index was used to evaluate the differentiation of the prediction results (a larger concordance index indicates a better prediction ability of the model). Integrated Brier Score was used to evaluate the calibration degree of the prediction (a smaller index indicates a better prediction ability). RESULTS: For data that met requirement of the proportion risk, the Cox_RCS model had the best prediction ability regardless of the sample size or deletion rate. For data that failed to meet the proportion risk, the prediction ability of Cox_DNN was optimal for a large sample size (≥500) with a low deletion (< 40%); the prediction ability of Cox_RCS was superior to those of other models in all other scenarios. For example data, the Cox_RCS model showed the best performance. CONCLUSION In analysis of nonlinear low maintenance data, Cox_RCS and Cox_DNN have their respective advantages and disadvantages in prediction. The conventional survival analysis methods are not inferior to machine learning or deep learning methods under certain conditions.
Proportional Hazards Models ; Survival Analysis ; Calibration ; Computer Simulation ; Data Analysis

OBJECTIVE: To explore the application and advantages of conditional inference forest in survival analysis. METHODS: We used simulated experiment and actual data to compare the predictive performance of 4 models, including Coxproportional hazards model, accelerated failure time model, random survival forest model and conditional inference forest model based on their Brier scores. RESULTS: Simulation experiment suggested that both of the two forest models had more accurate and robust predictive performance than the other two regression models. Conditional inference forest model was superior to the other models in analyzing time-to-event data with polytomous covariates, collinearity or interaction, especially for a large sample size and a high censoring rate. The results of actual data analysis demonstrated that conditional inference forest model had the best predictive performance among the 4 models. CONCLUSIONS Compared with the commonly used survival analysis methods, conditional inference forest model performs better especially when the data contain polytomous covariates with collinearity and interaction.
Data Analysis ; Proportional Hazards Models ; Sample Size ; Survival Analysis

Objective: To development the prognostic nomogram for malignant pleural mesothelioma (MPM). Methods: Two hundred and ten patients pathologically confirmed as MPM were enrolled in this retrospective study from 2007 to 2020 in the People's Hospital of Chuxiong Yi Autonomous Prefecture, the First and Third Affiliated Hospital of Kunming Medical University, and divided into training (n=112) and test (n=98) sets according to the admission time. The observation factors included demography, symptoms, history, clinical score and stage, blood cell and biochemistry, tumor markers, pathology and treatment. The Cox proportional risk model was used to analyze the prognostic factors of 112 patients in the training set. According to the results of multivariate Cox regression analysis, the prognostic prediction nomogram was established. C-Index and calibration curve were used to evaluate the model's discrimination and consistency in raining and test sets, respectively. Patients were stratified according to the median risk score of nomogram in the training set. Log rank test was performed to compare the survival differences between the high and low risk groups in the two sets. Results: The median overall survival (OS) of 210 MPM patients was 384 days (IQR=472 days), and the 6-month, 1-year, 2-year, and 3-year survival rates were 75.7%, 52.6%, 19.7%, and 13.0%, respectively. Cox multivariate regression analysis showed that residence (HR=2.127, 95% CI: 1.154-3.920), serum albumin (HR=1.583, 95% CI: 1.017-2.464), clinical stage (stage Ⅳ: HR=3.073, 95% CI: 1.366-6.910) and the chemotherapy (HR=0.476, 95% CI: 0.292-0.777) were independent prognostic factors for MPM patients. The C-index of the nomogram established based on the results of Cox multivariate regression analysis in the training and test sets were 0.662 and 0.613, respectively. Calibration curves for both the training and test sets showed moderate consistency between the predicted and actual survival probabilities of MPM patients at 6 months, 1 year, and 2 years. The low-risk group had better outcomes than the high-risk group in both training (P=0.001) and test (P=0.003) sets. Conclusion: The survival prediction nomogram established based on routine clinical indicators of MPM patients provides a reliable tool for prognostic prediction and risk stratification.
Humans ; Mesothelioma, Malignant ; Prognosis ; Nomograms ; Retrospective Studies ; Proportional Hazards Models