-
Application of Cox Regression in Modeling Survival Rate of Drug Abuse
Robert Kasisi,
Joseph Koske,
Mathew Kosgei
Issue:
Volume 7, Issue 1, January 2018
Pages:
1-7
Received:
28 June 2017
Accepted:
10 July 2017
Published:
20 December 2017
Abstract: Drug and substance abuse is a serious health problem in many countries. In Kenya drug abuse is one of the leading causes of mortality. Modeling the rate of survival of drug users involves determining time to relapse of drug users and the number of treatment episodes for full recovery. A study of the treatment programs that the subjects are enrolled was conducted. Those subjects who completed the treatment program and fully recovered from drug use were said to have survived while those who dropped out of the treatment program were said to have not survived. The objective of this study was to fit a cox repression model in determining a set of significant covariates for survival of drug users in Kenya. The dependent variable was survival time of the subject and the independent variables were age, gender, residence, marital status, job status, mode of drug abused and the type of drug abused. The study used data on drug use from Mathari National Hospital. Cox proportional hazards model was used to establish the hazard rate of a subject entering into drug use at different stages of life. Survival rate was 36.37% with the females having higher survival rates compared to male drug users. Age, gender, marital status and employment status were significant predictors of survival rate of drug users. The study recommended that subjects who were aged below 30 years, single and jobless required more intensive and specialized treatment. More intervention programs should be targeted to these subjects.
Abstract: Drug and substance abuse is a serious health problem in many countries. In Kenya drug abuse is one of the leading causes of mortality. Modeling the rate of survival of drug users involves determining time to relapse of drug users and the number of treatment episodes for full recovery. A study of the treatment programs that the subjects are enrolled...
Show More
-
Modeling Primary School Absenteeism and Academic Performance in Ethiopia: A Multivariate and Count Regression Models Approaches
Edossa Merga Terefe,
Zeytu Gashaw Asfaw
Issue:
Volume 7, Issue 1, January 2018
Pages:
8-20
Received:
21 November 2017
Accepted:
1 December 2017
Published:
5 January 2018
Abstract: School absenteeism and low academic performance at primary schools remain a big issue for developing countries like Ethiopia. Thus, this study aims to determine predicting factors influencing academic performances and school absenteeism jointly at primary schools in Ethiopia. A cross-sectional data were obtained from the Young Lives project from wave 1 (the starting month of academic year) and wave 2 (the last month of academic year). Multivariate regression model was used to investigate the predictors on the linear combination of academic performances and count regression model was also used to investigate the predictor of school absenteeism. In fact, both Poisson and Negative Binomial Regression Models were considered but the latter better fit to the data that have been used for this study than the former model. The result at national level showed mean of school absenteeism and academic performance at wave 1, respectively are 6 days and 67.64 scores and the average performance at wave 2 is also 61.44 score. It has been found out that the number of meals, number of siblings, mother’s literacy, survivor-ship of mother, type of school siblings attending, pre-school attendance, time to get to school, grade repeating, school drop outing, extra class attendances and the availability of helping person with school works at home have a combined effect on the school absenteeism and academic performance. Thus, potential stakeholders should pay attention for the aforementioned factors so as to reduce school absenteeism and then maximize academic performance.
Abstract: School absenteeism and low academic performance at primary schools remain a big issue for developing countries like Ethiopia. Thus, this study aims to determine predicting factors influencing academic performances and school absenteeism jointly at primary schools in Ethiopia. A cross-sectional data were obtained from the Young Lives project from wa...
Show More
-
Gaussian Longitudinal Analysis of Progression of Diabetes Mellitus Patients Using Fasting Blood Sugar Level: A Case of Debre Berhan Referral Hospital, Ethiopia
Wudneh Ketema Moges,
A. R. Muralidharan,
Haymanot Zeleke Tadesse
Issue:
Volume 7, Issue 1, January 2018
Pages:
21-28
Received:
21 November 2017
Accepted:
4 December 2017
Published:
9 January 2018
Abstract: Diabetes mellitus is a metabolic disorder where by glucose cannot effectively get transported out of the blood. It is a chronic disease with a high prevalence and growing concern in world wide. There are two Types of diabetes, which are Type I and Type II. A longitudinal data analysis retrospective based study was conducted between 1st September, 2012 to 30th August 2015 in Debre Berhan referral hospital. The main objective of the study was Gaussian longitudinal analysis of progression of Diabetes mellitus patients using fasting blood sugar level count following insulin, metformin and to identify factors predicting the progression of diabetic infection. A total of 248 Diabetes mellitus patients were included in the study whom 111 (44.8%) were females and the rest 137 (55.8%) were males. The generalized linear mixed model would be used to model the progression of diabetic infection. The appropriate variance covariance structure was Compound symmetry selected for this study. This study showed that age, sex, time, illiterate with time, primary with time, address with time, age with time and time with time were statistically significant factors for the progression of fasting blood sugar level at a logarithmic fasting sugar level over time in generalized linear mixed model. The mean fasting blood sugar level showed an increasing progress over time after patients were initiated on insulin and metformin. The statistical modelling approaches linear mixed model and generalized linear mixed model have been compared for the analysis of fasting data and we obtained generalized linear mixed model exhibited the best fit for this data with smaller disturbance than linear mixed model for their estimated standard error.
Abstract: Diabetes mellitus is a metabolic disorder where by glucose cannot effectively get transported out of the blood. It is a chronic disease with a high prevalence and growing concern in world wide. There are two Types of diabetes, which are Type I and Type II. A longitudinal data analysis retrospective based study was conducted between 1st September, 2...
Show More
-
A More Robust Random Effects Model for Disease Mapping
Tonui Benard Cheruiyot,
Mwalili Samuel,
Wanjoya Anthony
Issue:
Volume 7, Issue 1, January 2018
Pages:
29-34
Received:
20 December 2017
Accepted:
8 January 2018
Published:
19 January 2018
Abstract: Disease mapping studies have found wide applications within geographical epidemiology and public health and are typically analysed within a Bayesian hierarchical model formulation. The most popular disease mapping model is the Besag-York-Molli´e model. A distinguishing feature of this model is the use of two sets of random effects: one spatially structured to model spatial autocorrelation and the other spatially unstructured to describe residual unstructured heterogeneity. Very often the spatially unstructured random effect is assumed to be normally distributed. Under practical situations, this normality assumption is found to be over restrictive. In this study, we investigate a more robust spatially unstructured random effect distribution by considering the Inverse Gaussian (IG) distribution in the disease mapping problem. The distribution has the normal distribution as special case. The inferences under this model are carried out within a bayesian hierarchical model formulation implemented in WinBUGS. The IG distribution is introduced in WinBUGS using zero tricks. The usefulness of the proposed model is investigated with a simulation study and applied in real data; mapping HIV in Kenya. In this work we showed that the IG distribution can produce better results when the normality assumption is violated due to the skewness of the data. For the case of data in which the random effects are truly normal, the IG distribution adjusts to a normal distribution as dictated by the data itself. On the other hand, the spatially structured random effect is normally modelled using the intrinsic conditional autoregressive (iCAR) prior. This prior is improper and has the undesirable large scale property of leading to a negative pairwise correlation for regions located further apart. In addition, the BYM model presents some identifiability problems of the spatial and non-spatial effects. In this work, we consider Leroux CAR (named lCAR hereafter) prior, a less widely used prior in disease mapping, as the prior distribution for the spatially structured random effects.
Abstract: Disease mapping studies have found wide applications within geographical epidemiology and public health and are typically analysed within a Bayesian hierarchical model formulation. The most popular disease mapping model is the Besag-York-Molli´e model. A distinguishing feature of this model is the use of two sets of random effects: one spatially st...
Show More
-
Desirability and Design of Experiments Applied to the Optimization of the Reduction of Decarburization of the Process Heat Treatment for Steel Wire Sae 51B35
Cristie Diego Pimenta,
Messias Borges Silva,
Rose Lima de Morais Campos,
Walfredo Ribeiro de Campos Junior
Issue:
Volume 7, Issue 1, January 2018
Pages:
35-44
Received:
26 December 2017
Accepted:
10 January 2018
Published:
23 January 2018
Abstract: This study contributes directly to the understanding of the causative agent of loss of carbon steel wire during the heat treatment (phenomenon called decarburization). This carbon loss disqualifies the material for your applications originally envisaged, as with mechanical reduction of the amount of the chemical element carbon steel becomes less resistant to traction and less hard what would prevent your use for various applications mechanics. This research aim is to show desirability method application related to decarburization and hardness, in SAE 51B35 drawn steel wires. Data were generated from application of design of experiments methodology (by means of the Minitab Statistical Software) and results revealed that all variables considered in study have significant influence. Statistic modeling was carried out by means of application of multiple linear regression method which allowed obtaining models which represent properly the process itself. Results of response variables decarburization and hardness were submitted to desirability method application and the process was optimized at the best adjust condition of entry variables in relation to their specifications.
Abstract: This study contributes directly to the understanding of the causative agent of loss of carbon steel wire during the heat treatment (phenomenon called decarburization). This carbon loss disqualifies the material for your applications originally envisaged, as with mechanical reduction of the amount of the chemical element carbon steel becomes less re...
Show More
