A simulation study of a parametric mixture model of three different distributions to analyze heterogeneous survival data

In this paper a simulation study of a parametric mixture model of three different distributions is considered to model heterogeneous survival data.Some properties of the proposed parametric mixture of Exponential, Gamma and Weibull are investigated.The Expectation Maximization Algorithm (EM) is implemented to estimate the maximum likelihood estimators of three different postulated parametric mixture model parameters.The simulations are performed by simulating data sampled from a population of three component parametric mixture of three different distributions, and the simulations are repeated 10, 30, 50, 100 and 500 times to investigate the consistency and stability of the EM scheme.The EM Algorithm scheme developed is able to estimate the parameters of the mixture which are very close to the parameters of the postulated model.The repetitions of the simulation give parameters closer and closer to the postulated models, as the number of repetitions increases, with relatively small standard errors

Parametric mixture model of three components for modelling heterogeneos survival data

Previous studies showed that two components of survival mixture model performed better than pure classical parametric survival model. However there are crucial needs for three components of survival mixture model due to the behaviour of heterogeneous survival data which commonly comprises of more than two distributions. Therefore in this study two models of three components of survival mixture model were developed. Model 1 is three components of parametric survival mixture model of Gamma distributions and Model 2 is three components of parametric survival mixture model of Exponential, Gamma and Weibull distributions. Both models were estimated using the Expectation Maximization (EM) and validated via simulation and empirical studies. The simulation was repeated 300 times by incorporating three different sample sizes: 100, 200, 500; three different censoring percentages: 10%, 20%, 40%; and two different sets of mixing probabilities: ascending (10%, 40%, 50%) and descending (50%, 30%, 20%). Several sets of real data were used in the empirical study and models comparisons were implemented. Model 1 was compared with pure classical parametric survival model, two and four components parametric survival mixture models of Gamma distribution, respectively. Model 2 was compared with pure classical parametric survival models and three components parametric survival mixture models of the same distribution. Graphical presentations, log likelihood (LL), Akaike Information Criterion (AIC), Mean Square Error (MSE) and Root Mean Square Error (RMSE) were used to evaluate the performance. Simulation findings revealed that both models performed well at large sample size, small percentage of censoring and ascending mixing probabilities. Both models also produced smaller errors compared to other type of survival models in the empirical study. These indicate that both of the developed models are more accurate and provide better option to analyse heterogeneous survival data

Mixture model of the Exponential, Gamma and Weibull distributions to analyse heterogeneous survival data

Aims: In this study a survival mixture model of three components is considered to analyse survival data of heterogeneous nature.The survival mixture model is of the Exponential, Gamma and Weibull distributions.Methodology: The proposed model was investigated and the Maximum Likelihood (ML) estimators of the parameters of the model were evaluated by the application of the Expectation Maximization Algorithm (EM).Graphs, log likelihood (LL) and the Akaike Information Criterion (AIC) were used to compare the proposed model with the pure classical parametric survival models corresponding to each component using real survival data.The model was compared with the survival mixture models corresponding to each component.Results: The graphs, LL and AIC values showed that the proposed model fits the real data better than the pure classical survival models corresponding to each component.Also the proposed model fits the real data better than the survival mixture models corresponding to each component. Conclusion: The proposed model showed that survival mixture models are flexible and maintain the features of the pure classical survival model and are better option for modelling heterogeneous survival data

Prevalence and Risk Factors for Hepatitis B Infection among Pregnant Women attending Antenatal Clinic in UM Dafog Area, South Darfur State, Sudan

Background: Sudan is an endemic country for hepatitis B virus (HBV). Screening for HBV during pregnancy may help in instituting interventions to minimize vertical transmission. HBV in pregnant women in remote areas of Darfur were uncovered by the previous studies in Sudan, Objectives: This study was aimed to determine the seroprevalence of HBV and the possible risk factors for its acquisition among antenatal care attendants in Um Dafog area, South Darfur State, Sudan. Materials and Methods: A cross-sectional study was conducted between July, 2018 to January, 2019 in Um Dafog area. Blood samples were collected from165 (143from Sudan and 22from Central African Republic) pregnant women attending Antenatal Um Dafog Clinic. Serum was separated and tested for HBV marker using rapid HBsAg test. Information on socio-demographic and other pertinent data was collected using a structured questionnaire. Informed consent was obtained and confidentiality of personal data was assured.   Results: HBsAg was detected in 8.5% of study population. Participants from Central African Republic recorded higher prevalence (18.2%) than those from Sudan (7%). However, there was no significant association (P value > 0.05) between prevalence of HBsAg and residence, history of blood transfusion, tattooing, circumcision, surgical procedure, cupping therapy, caesarean section, ear piercing, unsafe injection, and jaundice or liver problems. Conclusion: The study concluded that the prevalence of HBV among pregnant women in Um Dafog area is of high endemicity according to WHO, hence the need for established public health interventions that leading  to reduction of HBV transmission. Keywords: Hepatitis B, pregnant women, Um Dafog, Sudan

Mixture Model of Different Distributions: A Simulation Study with Different Censoring and Mixing Probabilities

Survival mixture model of three different distributions was proposed. The model consists of a mixture of Exponential, Gamma and Weibull distributions. Simulated data was employed to investigate the performance of the model by considering three different censoring percentages and two sets of mixing probabilities in ascending order and descending order. The simulated data were used to estimate the maximum likelihood estimators of the model by employing Expectation Maximization (EM). Hazard functions corresponding to the censoring percentages were investigated graphically. Parameters of the proposed model were estimated and were all close the values used in generating the data. Simulation was repeated 300 times and the mean square error (MSE) and root mean square error (RMSE) were estimated to assess the consistency and stability of the model. The simulated data used to compare the effect of different censoring percentages revealed that the model performed much better with small percentage of censored observations. Also the model performed well with both the ascending and descending order of the mixing probabilities. However, mixing probabilities in ascending order performed better than the descending order. The hazard function graphs showed that, samples with higher percentage of censored observations seemed to have lower hazard compared to the smaller censored observations. The proposed model showed that survival mixture models are flexible and maintain the features of the pure classical survival model and are better option for modelling heterogeneous survival dat

A parametric mixture model of three different distributions: An approach to analyse heterogeneous survival data

A parametric mixture model of three different distributions is proposed to analyse heterogeneous survival data.The maximum likelihood estimators of the postulated parametric mixture model are estimated by applying an Expectation Maximization Algorithm (EM) scheme.The simulations are performed by generating data, sampled from a population of three component parametric mixture of three different distributions. The parameters estimated by the proposed EM Algorithm scheme are close to the parameters of the postulated model.To investigate the consistency and stability of the EM scheme, the simulations are repeated several times. The repetitions of the simulation gave parameters closer to the values of postulated models, with relatively small standard errors.Log likelihood, AIC and BIC are computed to compare the proposed mixture model with parametric mixture models of one distribution.The calculated values of Log likelihood, AIC and BIC are all infavour of the proposed parametric mixture model of different distributions

Assessment of the Learning styles of MBBS Students at the University of Kassala, Kassala State, Sudan, 2022

Introduction: Since the 1970s, Learning styles of health science students have been researched. It’s important to know the Learning styles of medical students these will be rewarding for both the instructor and the student. Objectives: This study aims to determine the learning styles of the medical students at the University of Kassala. Methodology: Cross sectional descriptive study conducted between 21/3/22 to 23/8/22 at the University of Kassala. After collecting the completed questionnaires, the data was analyzed using R language software Version R i386 4.0.2. Results: Out of 251 study sample, 228 responded with Questionnaire response rate of 90.8%. Wrong filled questionnaires were excluded resulting in 206 usable survey. 66.50485 % of our participants were females, 33.49515 % were males. The average age of the participants in years was 21.71359 +/- 1.933755. Reflectors (61.16505%) were the most prevalent learning style among medical students at the University of Kassala. Differences was noted in learning styles among the different sexes (p-value= 0.0371). No difference between the educational levels concerning the learning style (p-value = 0.09029). The living area does not affect the learning style (P-value = 0.3143). Conclusion and recommendations: Most of our students are reflectors, thus they prefer to watch rather than practice, they might be good at subjects that are more of watching type more than subjects that need practice. The results reveal the need to endorse self-directed learning. Further research is need to identify the most reliable teaching modality and instructional strategies. Keywords: Learning styles, Klob’s Inventory, Medical students DOI: 10.7176/JEP/13-30-01 Publication date:October 31st 202

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

Background: Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods: The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results: A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P < 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion: Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)

Investigation of alpha-Cellulose Local Sources for the Production of Nitrocellulose

The present study has aimed at investigating some local agricultural wastes as sources for the production of a-cellulose; these included: sugarcane bagasse, rice straw, durra stalk and groundnut shell. A mixture of toluene and ethanol in the ratio of 2:1 was utilized in solvent extraction, for 6 hr, to remove wax and resins, leaving the fibrous material. This was then delignified by treatment with acidic sodium chlorite (NaClO2) solution. Alkaline hydrolysis (18% NaOH) was employed to remove hemicelluloses leaving a-cellulose. The isolated final product was characterized using Fourier Transform Infrared (FTIR) Spectroscopy. The ?-Cellulose content of the four selected plants, (sugarcane-bagasse, rice straw, durra stalk and groundnut shell, were 40.3%, 39.3%, 42.7

Prevalence and Risk Factors for Hepatitis B Infection among Pregnant Women attending Antenatal Clinic in UM Dafog Area, South Darfur State, Sudan

Background: Sudan is an endemic country for hepatitis B virus (HBV). Screening for HBV during pregnancy may help in instituting interventions to minimize vertical transmission. HBV in pregnant women in remote areas of Darfur were uncovered by the previous studies in Sudan, Objectives: This study was aimed to determine the seroprevalence of HBV and the possible risk factors for its acquisition among antenatal care attendants in Um Dafog area, South Darfur State, Sudan.&#x0D; Materials and Methods: A cross-sectional study was conducted between July, 2018 to January, 2019 in Um Dafog area. Blood samples were collected from165 (143from Sudan and 22from Central African Republic) pregnant women attending Antenatal Um Dafog Clinic. Serum was separated and tested for HBV marker using rapid HBsAg test. Information on socio-demographic and other pertinent data was collected using a structured questionnaire. Informed consent was obtained and confidentiality of personal data was assured.  &#x0D; Results: HBsAg was detected in 8.5% of study population. Participants from Central African Republic recorded higher prevalence (18.2%) than those from Sudan (7%). However, there was no significant association (P value &gt; 0.05) between prevalence of HBsAg and residence, history of blood transfusion, tattooing, circumcision, surgical procedure, cupping therapy, caesarean section, ear piercing, unsafe injection, and jaundice or liver problems.&#x0D; Conclusion: The study concluded that the prevalence of HBV among pregnant women in Um Dafog area is of high endemicity according to WHO, hence the need for established public health interventions that leading  to reduction of HBV transmission. Keywords: Hepatitis B, pregnant women, Um Dafog, Sudan.</jats:p