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Prediction of Survival of HIV/AIDS Patients from Various Sources of Data Using AFT Models
Markos Abiso Erango,
Ayele Taye Goshu
Issue:
Volume 5, Issue 4, August 2017
Pages:
127-133
Received:
28 April 2017
Accepted:
16 May 2017
Published:
7 July 2017
Abstract: The aim of this paper is to predict and compare the survival of HIV/AIDS patients under ART follow-up in three different hospitals in Ethiopia. Three data sets with total 1304 patients were considered. Three parametric accelerated failure time distributions: lognormal, loglogistic and Weibull are used to analyze, predict and compare survival probabilities of the patients. The results indicate that the empirical hazard rates of the three data sets reveal maximal peaks. The patients from Arba Minch hospital seems to have highest event intensity. The AFT loglogistic model is selected to best fit to each of the data sets. Different covariates except TB infection status are found to affect patients' survival at each of the hospitals. Patients with TB infection at baseline tend to have shorter survival time as compare to one with no TB infection, with significant differences of survive time between the two groups. Patients under follow-up at Shashemene hospital tend have consistently highest survival probabilities in both TB positive and negative groups. Patients from Bale Robe hospital tend to have longest survival time, while those from Arba Minch hospital have shortest survival time. Patients with bedridden status have the shortest survival time. The AFT-loglogistic is recommended in modelling time-to-event data considered in this study. The results are unique to each hospital implying that patients' care and intervention needs to be specific.
Abstract: The aim of this paper is to predict and compare the survival of HIV/AIDS patients under ART follow-up in three different hospitals in Ethiopia. Three data sets with total 1304 patients were considered. Three parametric accelerated failure time distributions: lognormal, loglogistic and Weibull are used to analyze, predict and compare survival probab...
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Generalized Linear Models of Malaria Incidence in Jubek State, South Sudan
Issue:
Volume 5, Issue 4, August 2017
Pages:
134-138
Received:
8 May 2017
Accepted:
20 May 2017
Published:
7 July 2017
Abstract: Malaria is a leading cause of morbidity and mortality in South Sudan. This study is meant to focus on the trend of malaria incidence in Jubek state, South Sudan. Data on weekly malaria incidence for the period January 2011 to October 2015 were used in the study. Generalized linear models, Poisson and negative binomial regression models were employed to analyze the data. Results obtained suggest that malaria incidence has been still on increase by 0.0030 and 0.0032 per week respectively. Additionally, incidence rate ratio suggests an increase of 0.3% per week of malaria incidence in Jubek state. The study recommends malaria control programmes focused on reducing malaria incidence be introduced in South Sudan in general and in Jubek state in particular.
Abstract: Malaria is a leading cause of morbidity and mortality in South Sudan. This study is meant to focus on the trend of malaria incidence in Jubek state, South Sudan. Data on weekly malaria incidence for the period January 2011 to October 2015 were used in the study. Generalized linear models, Poisson and negative binomial regression models were employe...
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Variational Principles of Fuzzy Mappings and Its Applications
Yu-E Bao,
Ying-Chun Niu,
Yuan Li
Issue:
Volume 5, Issue 4, August 2017
Pages:
139-146
Received:
19 July 2017
Accepted:
Published:
19 July 2017
Abstract: In this paper, we firstly discuss the basic properties of the sub differential of fuzzy mapping and get some related conclusions. Secondly, we establish a variational principle of fuzzy mapping by establishing the concept of gauge fuzzy mapping. Then we prove the approximation sun rule of fuzzy mapping in sub-differential as the application of that principles.
Abstract: In this paper, we firstly discuss the basic properties of the sub differential of fuzzy mapping and get some related conclusions. Secondly, we establish a variational principle of fuzzy mapping by establishing the concept of gauge fuzzy mapping. Then we prove the approximation sun rule of fuzzy mapping in sub-differential as the application of that...
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On Maximum Likelihood Estimation for the Three Parameter Gamma Distribution Based on Left Censored Samples
Etienne Ouindllassida Jean Ouédraogo,
Blaise Somé,
Simplice Dossou-Gbété
Issue:
Volume 5, Issue 4, August 2017
Pages:
147-163
Received:
30 May 2017
Accepted:
14 June 2017
Published:
24 July 2017
Abstract: This paper deals with a Maximum likelihood method to fit a three-parameter gamma distribution to data from an independent and identically distributed scheme of sampling. The likelihood hinges on the joint distribution of the n − 1 largest order statistics and its maximization is done by resorting to a MM-algorithm. Monte Carlo simulations is performed in order to examine the behavior of the bias and the root mean square error of the proposed estimator. The performances of the proposed method is compared to those of two alternatives methods recently available in the literature: the location and scale parameters free maximum likelihood estimators (LSPF-MLE) of Nagatsuka & al. (2014), and Bayesian Likelihood (BL) method of Hall and Wang (2005). As in several papers on the three-parameter gamma fitting (Cohen and Whitten (1986), Tzavelas (2009), Nagatsuka & al. (2014), etc.), the classical dataset on the maximum flood levels data in millions of cubic feet per second for the Susquehanna River at Harrisburg, Pennsylvania, over 20 four-year periods from 1890–1969 from Antle and Dumonceaux’s paper (1973) is consider to illustrate the proposed method.
Abstract: This paper deals with a Maximum likelihood method to fit a three-parameter gamma distribution to data from an independent and identically distributed scheme of sampling. The likelihood hinges on the joint distribution of the n − 1 largest order statistics and its maximization is done by resorting to a MM-algorithm. Monte Carlo simulations is perfor...
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The Application of Binary Logistic Regression Analysis on Staff Performance Appraisal
Issue:
Volume 5, Issue 4, August 2017
Pages:
164-168
Received:
5 June 2017
Accepted:
14 June 2017
Published:
26 July 2017
Abstract: This study investigates and models salient factors that influences the performance of staff in an appraisal exercise and as well estimate the odds of these factors influencing the outcome variable (performance rating) as compared to their reference group or category. The Binary Logistic regression model was used to estimate chance of the staff given the influence of the identified independent variables. In the study, marital status was found to be significant in distinguishing staff performance as identified from the outlined factors influencing their performance.
Abstract: This study investigates and models salient factors that influences the performance of staff in an appraisal exercise and as well estimate the odds of these factors influencing the outcome variable (performance rating) as compared to their reference group or category. The Binary Logistic regression model was used to estimate chance of the staff give...
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