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Abstract : Validation of the Smooth Test of Goodness-of-Fit for Proportional Hazards in Cancer Survival Studies
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Abstract: In this study, we validate the smooth test of goodness-of-fit for the proportionality of the hazard function in the two-sample problem in cancer survival studies. The smooth test considered here is an extension of Neyman’s smooth test for proportional hazard functions. Simulations are conducted to compare the performance of the smooth test, the data-driven smooth test, the Kolmogorov-Smirnov proportional hazards test and the global test, in terms of power. Eight real cancer datasets from different settings are assessed for the proportional hazard assumption in the Cox proportional hazard models, for validation. The smooth test performed best and is independent of the number of covariates in the Cox proportional hazard models. Keywords: Cancer, Cox proportional hazards model, Global test, Neyman’s smooth test, Two-sample problem. |
Abstract : A Smooth Test of Goodness-of-Fit for the Weibull Distribution: An Application to an HIV Retention Data
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Abstract: In this study, we fit the two-parameter Weibull distribution to an HIV retention data and assess the fit using a smooth test of goodness-of-fit. The smooth test described here is a score test and is derived as an extension of the Neyman’s smooth test. Simulations are conducted to compare the power of the smooth test with the power of each of three empirical goodness-of-fit tests for the Weibull distribution. Results show that the smooth tests of order three and four are more powerful than the three empirical goodness-of-fit tests. For validation, we used retention data from an HIV care setting in Kenya. Keywords: Goodness-of-fit, Loss to follow-up, Neyman’s smooth test, Retention in HIV care, Weibull distribution. |
Abstract : Improving the Efficiency of Outpatient Services at Benue State University Teaching Hospital using the Queuing Theory
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Abstract: Introduction: Long client waiting time is a characteristic of poor performance of the health care delivery and is a major challenge for healthcare services all over the world, especially in developing countries. The study was aimed at developing a model that optimizes performance of the general outpatient department of the Benue State University Teaching Hospital, Makurdi, Benue State Nigeria. Methodology: Data was collected through observation and interviews with doctors at the general outpatient clinic of the Benue State University Teaching Hospital. The average number of clients seen per day was calculated by determining the average of daily attendants for five consecutive working days. The data obtained was used to create a five capacity scenarios using the queuing theory software. Result: The Average Daily Attendance (ADA) was 73.2 clients while the Average Daily Arrival Rate was 10.47 clients per hour. There were six doctors working on any given day in the clinic and a doctor spends an average of 16.2 minutes per patient, representing an average of 3.7 patients per hour. The model showed that the optimum system performance can be achieved with four doctors (with 70.7% server utilization rate, average of 1.065 clients on the queue and 0.102 hours waiting time). Conclusion: Four doctors working at the same time at the general outpatient clinic is required for optimal performance. The queuing theory should be used regularly at GOPD BSUTH and in all health facilities experiencing long queues to optimize operational efficiency. Keywords: Waiting time, Client satisfaction, server, performance, Makurdi. |
Abstract : Predictors of High Blood Pressure in South African Children: Quantile Regression Approach
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Abstract: Objective: To identify predictors of blood pressure (BP) in children and explore the predictors` effects on the conditional quantile functions of systolic blood pressure and diastolic blood pressure. Methods: A secondary data analysis was performed using data from the South African National Income Dynamics Study (2014-2015). From this particular secondary data, data for children aged between 10-17 years were extracted for analysis. The variables used in the study were systolic blood pressure (SBP), diastolic blood pressure (DBP), body mass index (BMI), age, smoking, alcohol consumption, exercises, gender and race. Two parameter estimation methods were used, ordinary least squares (OLS) and quantile regression (QR). Results: BMI had positive statistically significant estimated OLS and conditional quantile functions with both the BP measures except the 95th quantile for SBP. Age had also positive statistically significant estimated OLS and QR coefficients except for the 95th percentile, with both DBP and SBP respectively. Gender was found to be inversely related to both DBP and SBP except the 10th quantile for DBP. Race was partially significant to DBP. Smoking, alcohol consumption and exercises did not present any statistically significant relations with both DBP and SBP for all the estimated OLS and QR coefficients. Conclusion: BMI, age, gender and partially race were found to be predictors of BP in South African children using both OLS and QR techniques. Exercises, smoking and alcohol consumption did not present any statistically significant relations with both DBP and SBP probably because few participants exercise regularly, smoke and drink alcohol to bring out a significant change in both BP measurements. Keywords: Body Mass Index (BMI), Diastolic Blood Pressure (DBP), Systolic Blood Pressure (SBP), Ordinary Least Squares Regression (OLS), Quantile Regression (QR). |
