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 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

Musculoskeletal Discomfort And Its Associated Risk Factors Among Train Drivers This study was conducted to determine the prevalence and associated risk factors of musculoskeletal disorders among train drivers in Kuala Lumpur. In this cross-sectional study, 44 train drivers were interviewed using a self-administered questionnaire consists of socio-demographic information and occupational exposure; and a modified Nordic Standardised Questionnaire for questions related to musculoskeletal discomfort symptoms. Results showed that lower back (18.6%) are the most reported discomfort among train drivers, followed by neck (16.7%), knee (13.7%), and upper back (13.7%). Statistical analysis using Chi-square showed there is a significant association between discomfort in the neck with age (p<0.05), length of service (p<0.05), and the perception of driver’s seat comfort and suitability (P<0.001). Previous working experience, driving duration, and shift work were associated with shoulder, wrist and thigh discomfort. This study suggested that further investigation and early control measure need to be done to prevent the risk of the musculoskeletal problem among train drivers.
musculoskeletal disorders ; occupational hazards ; ergonomics ; freight ; cargo ; locomotive ; prevalence.

Safety is vital in any industry, including the offshore sector, which is classified as a major hazard industry. Health, Safety and the Environment (HSE) identified that the probability of accidents is high while working on the offshore sectors where it will exposed workers to many hazardous work activities. The appropriate measures to prevent accident in this sectors must be laid out clearly. This paper is to identify the effectiveness of safety awareness campaign and the continuity of the awareness among the workers to prevent injuries at offshore. To achieve this, we have identified the level of awareness and propose a guideline on areas of improvement. Prior of embarking to offshore, staff were exposed to safety awareness program for four weeks. After the program, we started with the pretest to all staff. They were posted offshore for 6 weeks. Within the period, the performance awareness of each staff is monitored through observation and interview. During the final week, the posttest questionnaire were administered to all staff. Two instruments were used for the quantitative data collection, which are Unsafe Act Unsafe Condition (UAUC) card; and Behavior Observation Tool (BOT) card. Questionnaire data were analyzed quantitatively. Paired-sample t-test was used for analyzing pre and post result. The results show that the mean was increased. Recent studies on the safety briefing highlighted several significant changes in terms of employee understanding toward safety. Safety awareness training has been introduced in the new safety briefing prior to offshore mobilization.
Offshore Sector ; HSE ; Hazards ; Unsafe Act/Unsafe Condition ; Behaviour Observation

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