Acceleration Factor

The shelf-life assessment of a product is essential to ensuring its safety and integrity. Shelf life is the period of time during which the product retains its required quality level under well-defined storage conditions. To assess the stability of a generic product, a stability test is required: the product is kept under different storage conditions and the performance of characteristics used to assess the quality of the product is monitored. Data collected through stability tests are then used to predict the product’s shelf life under further storage conditions a applying the calculated degradation rate. Kinetic models, such as the Arrhenius equation, are usually applied for this purpose. Since humidity can accelerate product degradation, it may be of interest to consider methods which quantify the effect of humidity. This paper proposes a comparison of several methods used to predict shelf life: the Bracket method, Eyring method, Peck method, Klinger method and Q-rule. An artificial case study is shown to compare the performance of the applied methods.

Female genital mutilation (FGM), also known as female genital cutting or female circumcision, is one of the deeply rooted traditional practices, in which the external female genital organ is either partially or totally removed for non-medical reasons. In Ethiopia, FGM is widespread across the majority of regions and ethnic groups, having the highest national prevalence that leads them to various complications such as immediate urinary and genital tract infection, pain and hemorrhage, complications in childbirth and social, psychological and sexual complications. This study aimed to model and investigate the potential risk factors of time-to-circumcision of girls in Ethiopia using parametric shared frailty models where regional states of the girls were used as a clustering effect in the models. The data source for the analysis was the 2016 EDHS data collected from January 18, 2016 up to June 27, 2016 from which the survival information of 2930 girls on age at circumcision obtained. The gamma and inverse Gaussian shared frailty distributions with Exponential, Weibull and log-logistic baseline models was employed to analyze risk factors associated with age at circumcision using socioeconomic and demographic factors. All the fitted models were compared by using AIC and BIC values from simulation study and actual dataset. The result revealed that about 22.4% of girls were circumcised and 77.6% were not circumcised. The median age at circumcision was 3 years. Based on AIC and BIC values from simulation experiment and graphical evidences, log-logistic model with inverse Gaussian shared frailty distribution preferred when compared with other models for age at circumcision dataset. The clustering effect was significant for modeling the determinants of time-to-circumcision of girls dataset. Based on the result of log-logistic inverse Gaussian shared frailty model, mothers and fathers educational level, place of residence and religion of parents were found to be the most significant determinants of age at circumcision of girls. The estimated acceleration factor for the group of mothers who had secondary and higher educational level were highly prolonged age at circumcision of girls by the factor of ϕ = 3.119 and ϕ = 3.933 respectively. The log-logistic model with inverse Gaussian shared frailty distribution described age at circumcision of girls better than other models and there was heterogeneity between the regions on age at circumcision. Improving parents access to education would be an important way approach for preventing girls' circumcision.

Correlated survival data with possible censoring are frequently encountered in survival analysis.When there are dependencies among observed survival times, conventional Cox proportionalhazards model (CPHM) and Accelerated Failure Time (AFT) models that assumes independentresponses may not be appropriate. In this study, we compare the performance of parametric andsemi-parametric survival models with application to clinical data. Specifically, the AFT modeland the CPHM with and without Random effect were compared. Data on hypertension wascollected from Federal Medical Centre Keffi and General Hospital Nasarawa for the period offive years (2016 – 2020). The results from the analysis revealed that the Weibull AFT modelwith Gamma Random effect distribution had the least AIC and BIC values indicating that itoutperformed the other models considered in this study. Hence, the interpretation of the resultswas based on the most efficient model. Based on our results, it was found that hypertens...

The proportional hazard regression models have been used extensively in survival analysis to understand and exploit the relationship between survival time and covariates. For left censored survival times, reversed hazard rate functions are more appropriate. In this paper, we discuss a parametric proportional reversed hazard rates model using exponential baseline. The estimation for the parameters are discussed. We also assess the performance of the proposed procedure based on a large number of Monte Carlo simulations. Finally, we illustrate the proposed method using a real case example and then we show that it provides a good and better fit than the usual proportional hazards model.

The Weibull multi-parameter regression (MPR) model with frailty is developed for interval censored survival data. The basic MPR model which is wholly parametric with non-proportional hazards was developed by Burke and MacKenzie in their 2016 Biometrics paper. We describe the basic model, develop the interval-censored likelihood and extend the model to include Gamma frailty. We present a simulation study and re-analyse data from the Signal Tandmobiel study. The MPR model is shown to be superior to a proportional hazards competitor.

Correlated survival data with possible censoring are frequently encountered in survival analysis.When there are dependencies among observed survival times, conventional Cox proportionalhazards model (CPHM) and Accelerated Failure Time (AFT) models that assumes independentresponses may not be appropriate. In this study, we compare the performance of parametric andsemi-parametric survival models with application to clinical data. Specifically, the AFT modeland the CPHM with and without Random effect were compared. Data on hypertension wascollected from Federal Medical Centre Keffi and General Hospital Nasarawa for the period offive years (2016 – 2020). The results from the analysis revealed that the Weibull AFT modelwith Gamma Random effect distribution had the least AIC and BIC values indicating that itoutperformed the other models considered in this study. Hence, the interpretation of the resultswas based on the most efficient model. Based on our results, it was found that hypertens...

Survival analysis is the analysis of statistical data in which the outcome variable of interest is time until an event occurs. In Statistical literature, it is observed that a good number of models have been developed for analyzing survival data or life time data .The most popular among them is the Cox Proportional Hazard (PH) model. Accelerated Failure Time (AFT) model, which is mainly used to study the reliability of industrial products can also be considered as a good alternative of Cox PH model in analyzing survival data. In this paper, the attempt has been made to present a review on Accelerated Failure Time models. Here the historical developments, technical developments and past research on AFT models are discussed.

IntroductionBreast cancer, the most common cause of cancer death and the most frequently diagnosed cancer among women worldwide, ranks as the second cause of death next to lung cancer. Thus, the main objective was to assess the factors that affect the survival time of breast cancer patients using the shared frailty model.MethodsA retrospective study design was used to collect relevant data on the survival time of breast cancer patients from the medical charts of 322 breast cancer patients under follow-up at the Felege Hiwot Comprehensive Specialized Hospital (FHCSH). The data were explored using the Cox proportional hazard model, the accelerated failure time model, and shared frailty models. The model comparison was done using AIC and BIC. As a result, the Weibull gamma shared frailty model had a minimum AIC and BIC value.ResultFrom a total of 322 patients, about 95 (29.5%) died and 227 (70.5%) were censored. The overall mean and median estimated survival times of breast cancer pati...

Marriage is an important part of human life and age at first marriage is the age at which individuals get married. This varies across communities and individuals in different country. Ethiopia is one of the Sub-Saharan Africa in which highest at early marriage and a small number of delayed marriages are occurred. Survival analysis is a statistical method for data analysis where the outcome variable of interest is the time to the occurrence of an event. Frailty model is an extension of Cox's proportional hazard model in which the hazard function depends upon an unobservable random quantity, the so-called frailty. Regional states of the women were used as a clustering effect in all frailty models. The study aimed to model the determinants of time-to-age at first marriage in Ethiopia. The data source for the analysis was the 2011 EDHS data collected during September 2010 through January 2011 from which the survival information of 12208 woman on age at first marriage. The gamma and inverse Gaussian shared frailty with exponential, Weibull and log-logistic baseline models was employed to analyze risk factors associated with age at first marriage using socioeconomic and demographic factors. All the fitted models were compared by using AIC. Out of the total, about 69.3% of women were married and 30.7% were not married at different age of marriage. The median of age at first marriage was 17 years. The log-logistic with inverse Gaussian shared frailty model had minimum value of AIC when compared with other models for age at first marriage dataset. The clustering effect was significant for modeling the determinants of time-to-age at first marriage dataset. Based on the result of log-logistic-inverse Gaussian shared frailty model, women educational level, head/parents occupation, place of residence, educational level of head/parents, access to media and respondent work status were found to be the most significant determinants of age at first marriage. The estimated acceleration factor for the group of women's who had secondary and higher educational level were highly prolonged age at first marriage by the factor of ϕ=1.0796 and ϕ=1.1497 respectively. The log-logistic with inverse Gaussian shared frailty model described age at first marriage dataset better than other models and there was heterogeneity between the regions on age at first marriage. Improving girls and young women access to education was an important avenue for rising women's age at first marriage and for empowering women.

Introduction Duration of breastfeeding is the length of the time that infants who were initially breastfed continue to receive breast milk until weaning. The duration of breastfeeding is important for a child's health, growth, and development. However, the duration of breastfeeding decreases from time to time and further leads children to be exposed to malnutrition (stunting, wasting, and weight loss). Children who did not get enough breastfeeding are also exposed to different diseases. Previous studies used a simple survival model and didn’t see the shared frailty model on the variable of interest. Therefore, the current study aimed to investigate the factors affecting the duration of breastfeeding among Ethiopian women of reproductive age with babies. Methods A cross-sectional study design was conducted on 15,400 women of childbearing age with babies in nine regional states and two city administrations. The data source for the analysis was the 2016 EDHS data. The Cox-proportio...

Correlated survival data with possible censoring are frequently encountered in survival analysis.When there are dependencies among observed survival times, conventional Cox proportionalhazards model (CPHM) and Accelerated Failure Time (AFT) models that assumes independentresponses may not be appropriate. In this study, we compare the performance of parametric andsemi-parametric survival models with application to clinical data. Specifically, the AFT modeland the CPHM with and without Random effect were compared. Data on hypertension wascollected from Federal Medical Centre Keffi and General Hospital Nasarawa for the period offive years (2016 – 2020). The results from the analysis revealed that the Weibull AFT modelwith Gamma Random effect distribution had the least AIC and BIC values indicating that itoutperformed the other models considered in this study. Hence, the interpretation of the resultswas based on the most efficient model. Based on our results, it was found that hypertens...

Studying the dynamics of spacing of births is important for several reasons including an understanding of completed family size. In this paper the length of birth interval between successive children that is called inter-birth interval in four disadvantaged regions of Ethiopia was considered. To identify and analyze socioeconomic and demographic factors that may have a significant influence on birth interval length in four disadvantaged regions of Ethiopia. M Ma at te er ri ia al l a an nd d M Me et th ho od ds s: : The data for the study was obtained from Ethiopian Demography and Health Survey data conducted in 2011. The data contains 3340 women aged from 15-49 years. The Kaplan-Meier and Cox's proportional hazards model were employed for the analysis of birth interval data using SPSS 16 and STATA 11 software. The Kaplan Meier median length was used to examine birth interval differentials by socio economic and demographic characteristics of women. R Re es su ul lt ts s: : The results indicated that almost in all birth intervals, educated women, rich women, orthodox, urban women, women belonging to Benishangul-gumuz region and women whose index child has survived have longer birth interval lengths. The Cox regression analyses revealed that region, place of residence and survival status of the index child were consistently significant while women educational level and wealth index were significant in some birth intervals. C Co on nc cl lu us si io on n: : Almost in all birth intervals, educated women, rich women, orthodox, urban women, women belonging to Benishangul-gumuz region and women whose index child has survived have longer birth interval lengths. K Ke ey y W Wo or rd ds s: : Birth intervals; disadvantaged regions; survival analysis; proportional hazards model Ö ÖZ ZE ET T A Am ma aç ç: : Doğum aralığı dinamiklerinin çalışılması, tamamlanmış aile büyüklüğünü de içeren birçok neden için önemlidir. Bu makalede birbirini izleyen çocuklar arasındaki doğum aralığının uzunluğu Etiyopya'da ki dört dezavantajlı bölgede dikkate alınan doğum aralığı olarak adlandırılmıştır. Sosyoekonomik ve demografik faktörleri belirlemede ve analiz etmede Etiyopya'daki dört dezavantajlı bölgede doğum aralığının anlamlı bir etkisi olabilir. G Ge er re eç ç v ve e Y Yö ön nt te em ml le er r: : Çalışmadaki veri seti, 2011'de yapılan Etiyopya Demografi ve Sağlık Çalışması'ndan elde edilmiştir. Veri, 15-49 yaş aralığında 3340 kadını içermektedir. Doğum aralığı verisinin analizi için SPSS 16 ve STATA 11 programları kullanılarak Kaplan-Meier ve Cox oransal hazard modeli kullanılmıştır. Kaplan-Meier medyan süresi, kadınların sosyoekonomik ve demografik karakterlerine göre doğum aralığındaki farklılıkları incelemek için kullanılmıştır. B Bu ul lg gu ul la ar r: : Sonuçlar, yaklaşık olarak tüm doğum aralıklarında eğitimli kadınlar, zengin kadınlar, ortodoks, şehirde yaşayan kadınlar, Benishangul-gumuz bölgesine mensup kadınlar ve çocukları sağ kalan kadınların doğum aralıklarından daha uzun olduğunu belirtmektedir. Cox regresyon analiz sonuçları, kadınların eğitim düzeyinin ve sağlık indekslerinin bazı doğum aralıklarında anlamlı olmasına rağmen bölgenin, indekslenen çocukların sağkalım durumunun ve ikamet yerinin tutarlı olarak anlamlı olduğunu açığa çıkarmıştır. S So on nu uç ç: : Yaklaşık olarak tüm doğum aralıkları, eğitimli kadınlar, zengin kadınlar, ortodoks, şehirde yaşayan kadınlar, Benishangul-gumuz bölgesine mensup kadınlar ve çocukları sağ kalan kadınlar daha uzun doğum aralıklarına sahiptir.

The proportional hazard regression models have been used extensively in survival analysis to understand and exploit the relationship between survival time and covariates. For left censored survival times, reversed hazard rate functions are more appropriate. In this paper, we discuss a parametric proportional reversed hazard rates model using exponential baseline. The estimation for the parameters are discussed. We also assess the performance of the proposed procedure based on a large number of Monte Carlo simulations. Finally, we illustrate the proposed method using a real case example and then we show that it provides a good and better fit than the usual proportional hazards model.

This study deals with simple Constant Stress Partially Accelerated life test (CSPALT) using type-II censoring. The lifetime distribution of the test item is assumed to follow Rayleigh distribution. The Maximum Likelihood (ML) Estimation is used to estimate the distribution parameters and acceleration factor. Asymptotic confidence interval estimates of the model parameters are also evaluated by using Fisher information matrix. In the last a simulation study is performed to illustrate the statistical properties of the parameters.

Background: Female genital mutilation affects the social, psychological, spiritual and physical well-being of women. In Ethiopia, studies regarding the female genital mutilation were conducted in various settings and years. Objective: This systematic review and meta-analysis was aimed to summarize the prevalence of female Genital Mutilation in Ethiopia. Methods: A systematic search of articles was conducted in PubMed, African Journals Online (AJOL), Excerpta Medica database (EMBASE), SCOPUS, Web of Science, and JSTOR. Data were extracted using a standardized data extraction format prepared in Microsoft Excel. The data were analyzed using STATA version 11 software. Cochrane Q statistic was used to assess the presence of significant between-study heterogeneity. I 2 was used to quantify between-study heterogeneity. A leave-one-out sensitivity analysis and subgroup analysis based on a study period and setting were done. The funnel plot and Egger's regression tests were used to measure the presence of substantial publication bias. The pooled estimated prevalence of female genital mutilation was conducted using a DerSimonian and Laird random effects model. Results: The pooled prevalence estimate of FGM was 77.28% (95% CI: 55.81, 98.76). The pooled prevalence was higher in studies conducted from 2013-2017 (78.39%, 95%CI: 48.24, 108.54) and studies conducted in Hospital (92.02: 95%CI: 55.81, 98.76). Conclusion: The prevalence of Female Genital Mutilation is high. Therefore, interventions that are focused on health education, social support and advocacy are recommended.

Background Both short and long birth intervals are associated with many risk factors and about 29% of births are short birth intervals in Ethiopia. The purpose of this study is to model the birth intervals of adult women aged 15–49 years using accelerated failure time and shared frailty models in order to analyze the birth intervals of Ethiopian women. Methods The data was obtained from the 2016 Ethiopian Demographic and Health Survey (EDHS). Accelerated failure time with different baseline and shared frailty models are used for the analysis to identify important demographic and socio-economic factors affecting the length of birth intervals and correlates of the birth intervals respectively. Results The data consists of 9147 women, of which about 7842 (85.5%) are closed interval and the rest of 1323(14.5%) are open interval. Accelerated failure time (AFT) result revealed that women education level, husbands education level, age at first birth, marital status, religion and family wea...

Introduction Duration of breastfeeding is the length of the time that infants who were initially breastfed continue to receive breast milk until weaning. The duration of breastfeeding is important for a child's health, growth, and development. However, the duration of breastfeeding decreases from time to time and further leads children to be exposed to malnutrition (stunting, wasting, and weight loss). Children who did not get enough breastfeeding are also exposed to different diseases. Previous studies used a simple survival model and didn’t see the shared frailty model on the variable of interest. Therefore, the current study aimed to investigate the factors affecting the duration of breastfeeding among Ethiopian women of reproductive age with babies. Methods A cross-sectional study design was conducted on 15,400 women of childbearing age with babies in nine regional states and two city administrations. The data source for the analysis was the 2016 EDHS data. The Cox-proportio...

Many model selection processes involve minimizing a loss function of the data over a set of models. A recent introduced approach is model selection curves, in which the selection criterion is expressed as a function of penalty multiplier in a linear (mixed) model or generalized linear model. In this article, we have adopted the model selection curves for accelerated failure time (AFT) models of survival data. In our simulation study, it was found that for data with small sample size and high proportion of censoring, the model selection curves approach outperformed the traditional model selection criteria, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). In other situations with respect to sample size and proportion of censoring, the model selection curves correctly identify the true model whether it is a full or reduced model. Moreover, through bootstrapping, it was shown that the model selection curves can be used to enhance the model selection process i...

Correlated survival data with possible censoring are frequently encountered in survival analysis.When there are dependencies among observed survival times, conventional Cox proportionalhazards model (CPHM) and Accelerated Failure Time (AFT) models that assumes independentresponses may not be appropriate. In this study, we compare the performance of parametric andsemi-parametric survival models with application to clinical data. Specifically, the AFT modeland the CPHM with and without Random effect were compared. Data on hypertension wascollected from Federal Medical Centre Keffi and General Hospital Nasarawa for the period offive years (2016 – 2020). The results from the analysis revealed that the Weibull AFT modelwith Gamma Random effect distribution had the least AIC and BIC values indicating that itoutperformed the other models considered in this study. Hence, the interpretation of the resultswas based on the most efficient model. Based on our results, it was found that hypertens...

In this study we introduce a likelihood-based method, via the Weibull and piecewise exponential distributions, capable of accommodating the dependence between failure and censoring times. The methodology is developed for the analysis of clustered survival data and it assumes that failure and censoring times are mutually independent conditional on a latent frailty. The dependent censoring mechanism is accounted through the frailty effect and this is accomplished by means of a key parameter accommodating the correlation between failure and censored observations. The full specification of the likelihood in our work simplifies the inference procedures with respect to Huang and Wolfe since it reduces the computation burden of working with the profile likelihood. In addition, the assumptions made for the baseline distributions lead to models with continuous survival functions. In order to carry out inferences, we devise a Monte Carlo EM algorithm. The performance of the proposed models is investigated through a simulation study. Finally, we explore a real application involving patients from the Dialysis Outcomes and Practice Patterns Study observed between 1996 and 2015.

Background: High maternal and child death with high fertility rate have been reported in Ethiopia. Extreme age at first birth is linked with both maternal and child morbidity and mortality. However, literatures showed there are limited studies on timing of first birth and its predictors in the area so far. Therefore, determining time to first birth and its predictors will help to design strategies to improve maternal and child survival. Methods: A survey-based study was conducted among reproductive-age women in Ethiopia using Ethiopian demographic health survey (EDHS), 2016 data. Stratified two-stage cluster sampling technique was used for sampling. The Kaplan-Meier (KM) method was used to estimate time to first birth. Inverse Weibull gamma shared frailty model applied to model the data at 95% confidence interval (CI), adjusted hazard ratio (AHR) and median hazard ratio (MHR) were reported as effect size. Proportional hazard assumption checked using Schoenfeld residual test. Informa...

In the analysis of clustered survival data, shared frailty models are often used when observations in the same group share common unknown risk factors or frailty. There is dependence in the event times belonging to the same group, while event times from different groups are conditionally independent given their covariates. In such models, the known effect on survival time is described using the baseline distribution and regression coefficients while the unknown effect is described through a frailty distribution. In this paper, the Gompertz, log-logistic, and generalized exponential distributions are studied as baseline distributions, under a shared frailty effect described by the generalized gamma distribution. Their hazard functions have been compared and their applicability under different settings and performance with generalized gamma frailty has been explored. These models are fitted to three real life datasets using Bayesian estimation methods and compared using the Bayesian I...

In developing countries about 3.5% of children aged 0-5 years are victims of severe acute malnutrition (SAM). Once the morbidity has developed the cure process takes variable period depending on various factors. Knowledge of time-to-cure from SAM will enable health care providers to plan resources and monitor the progress of cases with SAM. The current analysis presents modeling time-to-cure from SAM starting from the day of diagnosis in Wolisso St. Luke Catholic hospital, southwest Ethiopia. Methods: With the aim of coming up with appropriate survival (time-to-event) model that describes the SAM dataset, various parametric clustered time-to-event (frailty) models were compared. Frailty model, which is an extension of the proportional hazards Cox survival model, was used to analyze time-to-cure from SAM. Kebeles (villages) of the children were considered as the clustering variable in all the models. We used exponential, weibull and log-logistic as baseline hazard functions and the gamma as well as inverse Gaussian for the frailty distributions and then based on AIC criteria, all models were compared for their performance. The median time-to-cure from SAM cases was 14 days with the maximum of 63 days of which about 83% were cured. The log-logistic model with inverse Gaussian frailty has the minimum AIC value among the models compared. The clustering effect was significant in modeling time-to-cure from SAM. The results showed that age of a child and co-infection were the determinant prognostic factors for SAM, but sex of the child and the type of malnutrition were not significant. Conclusions: The log-logistic with inverse Gaussian frailty model described the SAM dataset better than other distributions used in this study. There is heterogeneity between the kebeles in the time-to-cure from SAM, indicating that one needs to account for this clustering variable using appropriate clustered time-to-event frailty models.

ptimum Power Flow (OPF) studies to minimize either the power distribution losses and the cost of power drawn from the substation, without affecting on the voltage regulation. This paper discusses the concept of the continuation Gauss-Seidel method to be used with load flow analysis control for stability of large power systems. This method preserves load flow equations and hence can achieve better accuracy, which is verified by the case studies of an IEEE-5 busses system.

Background Both short and long birth intervals are associated with many risk factors and about 29% of births are short birth intervals in Ethiopia. The purpose of this study is to model the birth intervals of adult women aged 15–49 years using accelerated failure time and shared frailty models in order to analyze the birth intervals of Ethiopian women. Methods The data was obtained from the 2016 Ethiopian Demographic and Health Survey (EDHS). Accelerated failure time with different baseline and shared frailty models are used for the analysis to identify important demographic and socio-economic factors affecting the length of birth intervals and correlates of the birth intervals respectively. Results The data consists of 9147 women, of which about 7842 (85.5%) are closed interval and the rest of 1323(14.5%) are open interval. Accelerated failure time (AFT) result revealed that women education level, husbands education level, age at first birth, marital status, religion and family wea...

ptimum Power Flow (OPF) studies to minimize either the power distribution losses and the cost of power drawn from the substation, without affecting on the voltage regulation. This paper discusses the concept of the continuation Gauss-Seidel method to be used with load flow analysis control for stability of large power systems. This method preserves load flow equations and hence can achieve better accuracy, which is verified by the case studies of an IEEE-5 busses system.

Female genital mutilation (FGM), also known as female genital cutting or female circumcision, is one of the deeply rooted traditional practices, in which the external female genital organ is either partially or totally removed for non-medical reasons. In Ethiopia, FGM is widespread across the majority of regions and ethnic groups, having the highest national prevalence that leads them to various complications such as immediate urinary and genital tract infection, pain and hemorrhage, complications in childbirth and social, psychological and sexual complications. This study aimed to model and investigate the potential risk factors of time-to-circumcision of girls in Ethiopia using parametric shared frailty models where regional states of the girls were used as a clustering effect in the models. The data source for the analysis was the 2016 EDHS data collected from January 18, 2016 up to June 27, 2016 from which the survival information of 2930 girls on age at circumcision obtained. T...

Female genital mutilation (FGM), also known as female genital cutting or female circumcision, is one of the deeply rooted traditional practices, in which the external female genital organ is either partially or totally removed for non-medical reasons. In Ethiopia, FGM is widespread across the majority of regions and ethnic groups, having the highest national prevalence that leads them to various complications such as immediate urinary and genital tract infection, pain and hemorrhage, complications in childbirth and social, psychological and sexual complications. This study aimed to model and investigate the potential risk factors of time-to-circumcision of girls in Ethiopia using parametric shared frailty models where regional states of the girls were used as a clustering effect in the models. The data source for the analysis was the 2016 EDHS data collected from January 18, 2016 up to June 27, 2016 from which the survival information of 2930 girls on age at circumcision obtained. T...

The GS-distribution is a family of distributions that provide an accurate representation of any unimodal univariate continuous distribution. In this contribution we explore the utility of this family as a general model in survival analysis. We show that the survival function based on the GS-distribution is able to provide a model for univariate survival data and that appropriate estimates can be obtained. We develop some hypotheses tests that can be used for checking the underlying survival model and for comparing the survival of different groups.

The GS-distribution is a family of distributions that provide an accurate representation of any unimodal univariate continuous distribution. In this contribution we explore the utility of this family as a general model in survival analysis. We show that the survival function based on the GS-distribution is able to provide a model for univariate survival data and that appropriate estimates can be obtained. We develop some hypotheses tests that can be used for checking the underlying survival model and for comparing the survival of different groups.

First birth interval is one of the examples of survival data. One of the characteristics of survival data is its observation period that is fully unobservable or censored. Analyzing the censored data using ordinary methods will lead to bias, so that reducing such bias required a certain method called survival analysis. There are two methods used in survival analysis that are parametric and non-parametric method. The objective of this paper is to determine the appropriate method for modeling the birth of the first child. The exponential model with the inclusion of covariates is used as parametric method, considering that the newly married couples tend to have desire for having baby as soon as possible and such desire will be weakened by increasing age of marriage. The data that will be analyzed were taken from the Indonesia Demographic and Health Survey (IDHS) 2012. The result of data analysis shows that the birth of the first child data is not exponentially distributed thus the Cox proportional hazard method is used. Because of the suspicion that disproportional covariate exists, then the proportional hazard test is conducted to show that the covariate of age is not proportional, the generalized Cox proportional method is used, namely Cox extended that allows the inclusion of disproportional covariates. The result of analysis using Cox extended model indicates that the factors affecting the birth of the first child in Indonesia are the area of residence, educational history and its age.

There are several models for the calculation of the acceleration factor for the electronic assemblies subjected to stress tests. A correct reliability assessment requires an accurate analysis of these models and the evaluation of their real effectiveness and adequacy. As regard to the solder joints subjected to the thermomechanical stresses given by temperature variation, applicable models are the original Coffin-Manson model, the Coffin-Manson model modified by Norris and Landzberg, and the model described in the standard IPC-SM-785. The last one is the most complete model to evaluate the acceleration factors given by slow temperature swings. However, the most effective method for the estimate of the solder joint aging is based on the analysis of the microstructure of the alloy. Regarding the acceleration factors of the electronic components, the international standard IEC 61709 can be used. This standard provides the equations to determine the intrinsic failure rates of the electronic components. Those equations allow to calculate the acceleration factors for the different types of stress (caused by temperature, voltage, current, etc.). This standard is more linked to the actual characteristics of the components than MIL-STD-217F, which was widespread in the past. IEC 61709 is also a useful tool for the design of electronic assemblies. A new method for the calculation of the acceleration factors is the analysis of the variation of the electrical parameters of the assembly when the stress is applied. This new method allows to evaluate directly the effects of the stress on the functionality of the assembly.

Female genital mutilation (FGM), also known as female genital cutting or female circumcision,
is one of the deeply rooted traditional practices, in which the external female genital
organ is either partially or totally removed for non-medical reasons. In Ethiopia, FGM is
widespread across the majority of regions and ethnic groups, having the highest national
prevalence that leads them to various complications such as immediate urinary and genital
tract infection, pain and hemorrhage, complications in childbirth and social, psychological
and sexual complications. This study aimed to model and investigate the potential risk
factors of time-to-circumcision of girls in Ethiopia using parametric shared frailty models
where regional states of the girls were used as a clustering effect in the models. The data
source for the analysis was the 2016 EDHS data collected from January 18, 2016 up to
June 27, 2016 from which the survival information of 2930 girls on age at circumcision
obtained. The gamma and inverse Gaussian shared frailty distributions with Exponential,
Weibull and log-logistic baseline models was employed to analyze risk factors associated
with age at circumcision using socio-economic and demographic factors. All the fitted
models were compared by using AIC and BIC values from simulation study and actual
dataset. The result revealed that about 22.4% of girls were circumcised and 77.6% were
not circumcised. The median age at circumcision was 3 years. Based on AIC and BIC values
from simulation experiment and graphical evidences, log-logistic model with inverse
Gaussian shared frailty distribution preferred when compared with other models for age at
circumcision dataset. The clustering effect was significant for modeling the determinants of
time-to-circumcision of girls dataset. Based on the result of log-logistic inverse Gaussian
shared frailty model, mothers and fathers educational level, place of residence and religion
of parents were found to be the most significant determinants of age at circumcision of
girls. The estimated acceleration factor for the group of mothers who had secondary and
higher educational level were highly prolonged age at circumcision of girls by the factor of
ϕ = 3.119 and ϕ = 3.933 respectively. The log-logistic model with inverse Gaussian shared
frailty distribution Female genital mutilation (FGM), also known as female genital cutting or female circumcision,
is one of the deeply rooted traditional practices, in which the external female genital
organ is either partially or totally removed for non-medical reasons. In Ethiopia, FGM is
widespread across the majority of regions and ethnic groups, having the highest national
prevalence that leads them to various complications such as immediate urinary and genital
tract infection, pain and hemorrhage, complications in childbirth and social, psychological
and sexual complications. This study aimed to model and investigate the potential risk
factors of time-to-circumcision of girls in Ethiopia using parametric shared frailty models
where regional states of the girls were used as a clustering effect in the models. The data
source for the analysis was the 2016 EDHS data collected from January 18, 2016 up to
June 27, 2016 from which the survival information of 2930 girls on age at circumcision
obtained. The gamma and inverse Gaussian shared frailty distributions with Exponential,
Weibull and log-logistic baseline models was employed to analyze risk factors associated
with age at circumcision using socio-economic and demographic factors. All the fitted
models were compared by using AIC and BIC values from simulation study and actual
dataset. The result revealed that about 22.4% of girls were circumcised and 77.6% were
not circumcised. The median age at circumcision was 3 years. Based on AIC and BIC values
from simulation experiment and graphical evidences, log-logistic model with inverse
Gaussian shared frailty distribution preferred when compared with other models for age at
circumcision dataset. The clustering effect was significant for modeling the determinants of
time-to-circumcision of girls dataset. Based on the result of log-logistic inverse Gaussian
shared frailty model, mothers and fathers educational level, place of residence and religion
of parents were found to be the most significant determinants of age at circumcision of
girls. The estimated acceleration factor for the group of mothers who had secondary and
higher educational level were highly prolonged age at circumcision of girls by the factor of
ϕ = 3.119 and ϕ = 3.933 respectively. The log-logistic model with inverse Gaussian shared
frailty distribution described age at circumcision of girls better than other models and there
was heterogeneity between the regions on age at circumcision. Improving parents access to
education would be an important way approach for preventing girls’ circumcision.described age at circumcision of girls better than other models and there
was heterogeneity between the regions on age at circumcision. Improving parents access to
education would be an important way approach for preventing girls’ circumcision.

ptimum Power Flow (OPF) studies to minimize either the power distribution losses and the cost of power drawn from the substation, without affecting on the voltage regulation. This paper discusses the concept of the continuation Gauss-Seidel method to be used with load flow analysis control for stability of large power systems. This method preserves load flow equations and hence can achieve better accuracy, which is verified by the case studies of an IEEE-5 busses system.

First birth interval is one of the examples of survival data. One of the characteristics of survival data is its observation period that is fully unobservable or censored. Analyzing the censored data using ordinary methods will lead to bias, so that reducing such bias required a certain method called survival analysis. There are two methods used in survival analysis that are parametric and non-parametric method. The objective of this paper is to determine the appropriate method for modeling the birth of the first child. The exponential model with the inclusion of covariates is used as parametric method, considering that the newly married couples tend to have desire for having baby as soon as possible and such desire will be weakened by increasing age of marriage. The data that will be analyzed were taken from the Indonesia Demographic and Health Survey (IDHS) 2012. The result of data analysis shows that the birth of the first child data is not exponentially distributed thus the Cox proportional hazard method is used. Because of the suspicion that disproportional covariate exists, then the proportional hazard test is conducted to show that the covariate of age is not proportional, the generalized Cox proportional method is used, namely Cox extended that allows the inclusion of disproportional covariates. The result of analysis using Cox extended model indicates that the factors affecting the birth of the first child in Indonesia are the area of residence, educational history and its age.

Survival analysis is a branch of statistics, which is focussed on the analysis of time-to-event data. In multivariate survival analysis, the proportional hazards (PH) is the most popular model in order to analyze the effects of several covariates on the survival time. However, the assumption of constant hazards in PH model is not always satisfied by the data. The violation of the PH assumption leads to the misinterpretation of the estimation results and decreasing the power of the related statistical tests. On the other hand, the accelerated failure time (AFT) models do not assume the constant hazards in the survival data as in PH model. The AFT models, moreover, can be used as the alternative to PH model if the constant hazards assumption is violated. The objective of this research was to compare the performance of PH model and the AFT models in analyzing the significant factors affecting the first birth interval (FBI) data in Indonesia. In this work, the discussion was limited to three AFT models which were based on Weibull, exponential, and log-normal distribution. The analysis by using graphical approach and a statistical test showed that the non-proportional hazards exist in the FBI data set. Based on the Akaike information criterion (AIC), the log-normal AFT model was the most appropriate model among the other considered models. Results of the best fitted model (log-normal AFT model) showed that the covariates such as women's educational level, husband's educational level, contraceptive knowledge, access to mass media, wealth index, and employment status were among factors affecting the FBI in Indonesia.

Monitoring surgical processes has gained prominence by accounting for patients ̛ health condition prior to surgery in recent years. However, most of previous researchers have focused on Phase-II monitoring based on binary outcomes, while very little attention has been paid to Phase-I monitoring procedures, especially, when the outcomes are continuous. In this paper, a general Phase-I accelerated failure time (AFT)-based risk-adjusted control chart is proposed to monitor continuous surgical outcomes based on a likelihood-ratio test derived from a change-point model. Different from the existing models, this paper shows that continuous outcomes depend not only on the patient conditions described by the Parsonnet score, but also is dependent on other categorical operational covariates such as surgeons. The proposed risk-adjustment model is fitted by incorporating dummy variables to reflect different surgeon groups as a categorical covariate. We show that the categorical covariate can effectively reflect the inherent data heterogeneity, thus can improve the estimation of the parameters of the risk-adjustment model and hence can enhance the detection power of the proposed risk-adjusted control chart.

This paper proposes an improved load flow method based on perturbation theory which is simple, efficient and reliable. The performance of the proposed method has been tested on well-conditioned IEEE 5-bus, 14-bus, 30 bus and also tested on 11-bus ill conditioned test systems. The proposed method shows its efficacy and robustness with minimum number of iterations along with high accuracy on conventional load flow methods like G-S (Gauss-Siedal) method.

Background: Although neonatal mortality estimates continue to decline in Ethiopia over
the years, it is a matter of a great concern among stake holders as the decline is not
enough to reduce NM.
Objective: The study aimed to investigate significant factors and appropriate model of
neonatal mortality in Ethiopia and also to assess the effect region as a cluster.
Method: The data was obtained from the EDHS, 2011.The study sample (n = 2604) was
based on infants (0–1 months old) during the survey period; extracted from the women
data base. Two model families, generalized estimating equation and alternating logistic
regression models from marginal model family, and generalized linear mixed model from
cluster specific model family were used for the analysis. AIC and QIC were used for
model selection.
Result: the result showed that among eligible children the proportion of NM was 14.78%.
Alternating logistic regression model was best fits the data for population-averaged
effects of the given factors on neonatal mortality than generalized estimating equation
model and generalized linear mixed model with two random intercepts was the best
model to evaluate within and between regional heterogeneity of neonatal mortality. From
all the fitted model age of respondents (mothers), multiplicity of birth, birth interval, and
birth order, age at first birth, residence, and birth size were found to be significant factors
of neonatal mortality; whereas wealth, mothers educational level, sex of a child and place
of delivery were non-significant factors.
Conclusion: More importantly, this study contributes to the understanding of the
individual and collective effect of maternal, socio-economic and child related factors
influencing neonatal mortality in Ethiopia.

This paper proposesan improved load flow method based on perturbation theory which is simple, efficient and reliable. The performance of the proposed method has been tested on well-conditioned IEEE 5-bus, 14-bus, 30 bus and also tested on 11-bus ill conditioned test systems. The proposed method shows its efficacy and robustness with minimum number of iterations along with high accuracy on conventional load flow methods like G-S (Gauss-Seidel) method.