Time to Kidneys Failure Modeling in the Patients at Adama Hospital Medical College: Application of Copula Model.

BACKGROUND: Kidney failure is a common public health problem around the world. The vast majority of kidney failure cases in Sub-Saharan African nations, including Ethiopia, go undetected and untreated, resulting in practically certain mortality cases. This study was aimed primarily to model the time to (right and left) kidneys failure in the patients at Adama Hospital Medical College using the copula model. STUDY DESIGN: A retrospective cohort study. METHODS: The copula model was used to examine join time to the right and left kidneys failure in the patients by specifying the dependence between the failure times. We employed Weibull, Gompertz, and Log-logistic marginal baseline distributions with Clayton, Gumbel, and Joe Archimedean copula families. RESULTS: This research comprised a total of 431 patients, out of which, 170 (39.4%) of the total patients failed at least one kidney during the follow-up period. Factors such as sex, age, family history of kidney disease, diabetes mellitus, hypertension, and obesity were found to be the most predictive variables for kidney failure in the patients. There was a 41 percent correlation between the patients' time to the right and left kidneys failure. CONCLUSION: The patients' kidney failure risk factors included being a male, older adult, obese, hypertensive, diabetic and also having a family history of kidney disease. The dependence between the patient's time to the right and left kidneys failure was strong. The best statistical model for describing the kidney failure datasets was the log-logistic-Clayton Archimedean copula model.

Cardiovascular Disease Risk Factors in Hypertensive Patients: A Case Study of Jimma University Medical Center.

BACKGROUND: Cardiovascular diseases are a group of disorders of the heart and blood vessels. Globally an estimated 17.9 million people died from cardiovascular diseases, which covers 31% of all global deaths, and the three quarters taking place in developing countries. Hypertension is the major cause of cardiovascular diseases. Its influence is high with other risk factors. This study aimed to determine the major risk factors of cardiovascular disease among hypertensive patients at Jimma University Medical Center. METHODS: Using December to January 2017 hypertension-related report of Jimma University Medical Center (JUMC), a retrospective cohort study type was conducted on purposively selected 343 patients. Three nurses from JUMC participated in the data collection, and the data were fitted using the Cox-Proportional Hazard (Cox-PH) model. RESULTS: About 138 (40.23%) patients were experienced cardiovascular disease at 28 months median time. From the Cox-PH model, the hazard ratio and 95% CI of age (HR = 1.0495, 95% CI: 1.0250-1.0747), urban (HR = 2.1225, 95% CI: 1.3813-3.2613), diabetes mellitus (HR = 1.702, 95% CI: 1.0082-2.8731), proteinuria (HR = 1.8749, 95% CI: 1.2675-2.7734), two drug users (HR = 0.2533, 95% CI: 0.1376-0.4662), systolic blood pressure (HR = 1.0343, 95% CI: 1.0147-1.0542) and pulse rate (HR = 1.0111, 95% CI: 0.9933-1.0293). CONCLUSION: The presence of proteinuria, diabetes mellitus, and being an urban resident had a great impact on the cardiovascular diseases of hypertensive patients.

The power prior with multiple historical controls for the linear regression model.

Combining historical control data with current control data may reduce the necessary study size of a clinical trial. However, this only applies when the historical control data are similar enough to the current control data. Several Bayesian approaches for incorporating historical data in a dynamic way have been proposed, such as the meta-analytic-predictive (MAP) prior and the modified power prior (MPP). Here we discuss the generalization of the MPP approach for multiple historical control groups for the linear regression model. This approach is useful when the controls differ more than in a random way, but become again (approximately) exchangeable conditional on covariates. The proposed approach builds on the approach previously developed for binary outcomes by some of the current authors. Two MPP approaches have been developed with multiple controls. The first approach assumes independent powers, while in the second approach the powers have a hierarchical structure. We conducted several simulation studies to investigate the frequentist characteristics of borrowing methods and analyze a real-life data set. When there is between-study variation in the slopes of the model or in the covariate distributions, the MPP approach achieves approximately nominal type I error rates and greater power than the MAP prior, provided that the covariates are included in the model. When the intercepts vary, the MPP yields a slightly inflated type I error rate, whereas the MAP does not. We conclude that our approach is a worthy competitor to the MAP approach for the linear regression case.

Determinant of time-to-first birth interval after marriage among Ethiopian women.

BACKGROUND: Time-to-first birth after marriage has a significant role in the future life of each individual woman and has a direct relationship with fertility. This study aimed to see the determinant of time-to-first birth interval after marriage among Ethiopian women. METHODS: The data was obtained from 2011 Ethiopia Demographic and Health Survey which is the third survey. The sample was selected using a stratified; two-stage cluster sampling design and the data was analysed using parametric shared frailty model. RESULTS: A total of 7925 ever married women from the nine region of the country were included in this study. Of the total women, 5966 (75.3%) of them gave firstbirth. Age, residence area, employment status, contraceptive use and education of women were associated significantly to time-to-first birth. CONCLUSIONS: Women having younger age at first marriage, urban women, contraceptive users had prolonged time to first birth interval. There is a need of teaching family for contraceptive use and improving women education to increase the length of first birth interval in Ethiopia.

Modified power prior with multiple historical trials for binary endpoints.

Including historical data may increase the power of the analysis of a current clinical trial and reduce the sample size of the study. Recently, several Bayesian methods for incorporating historical data have been proposed. One of the methods consists of specifying a so-called power prior whereby the historical likelihood is downweighted with a weight parameter. When the weight parameter is also estimated from the data, the modified power prior (MPP) is needed. This method has been used primarily when a single historical trial is available. We have adapted the MPP for incorporating multiple historical control arms into a current clinical trial, each with a separate weight parameter. Three priors for the weights are considered: (1) independent, (2) dependent, and (3) robustified dependent. The latter is developed to account for the possibility of a conflict between the historical data and the current data. We analyze two real-life data sets and perform simulation studies to compare the performance of competing Bayesian methods that allow to incorporate historical control patients in the analysis of a current trial. The dependent power prior borrows more information from comparable historical studies and thereby can improve the statistical power. Robustifying the dependent power prior seems to protect against prior-data conflict.

Risk Factors for Mortality among Adult HIV/AIDS Patients Following Antiretroviral Therapy in Southwestern Ethiopia: An Assessment through Survival Models.

Introduction: Efforts have been made to reduce HIV/AIDS-related mortality by delivering antiretroviral therapy (ART) treatment. However, HIV patients in resource-poor settings are still dying, even if they are on ART treatment. This study aimed to explore the factors associated with HIV/AIDS-related mortality in Southwestern Ethiopia. Method: A non-concurrent retrospective cohort study which collected data from the clinical records of adult HIV/AIDS patients, who initiated ART treatment and were followed between January 2006 and December 2010, was conducted, to explore the factors associated with HIV/AIDS-related mortality at Jimma University Specialized Hospital (JUSH). Survival times (i.e., the time from the onset of ART treatment to the death or censoring) and different characteristics of patients were retrospectively examined. A best-fit model was chosen for the survival data, after the comparison between native semi-parametric Cox regression and parametric survival models (i.e., exponential, Weibull, and log-logistic). Result: A total of 456 HIV patients were included in the study, mostly females (312, 68.4%), with a median age of 30 years (inter-quartile range (IQR): 23-37 years). Estimated follow-up until December 2010 accounted for 1245 person-years at risk (PYAR) and resulted in 66 (14.5%) deaths and 390 censored individuals, representing a median survival time of 34.0 months ( IQR: 22.8-42.0 months). The overall mortality rate was 5.3/100 PYAR: 6.5/100 PYAR for males and 4.8/100 PYAR for females. The Weibull survival model was the best model for fitting the data (lowest AIC). The main factors associated with mortality were: baseline age (>35 years old, AHR = 3.8, 95% CI: 1.6-9.1), baseline weight (AHR = 0.93, 95% CI: 0.90-0.97), baseline WHO stage IV (AHR = 6.2, 95% CI: 2.2-14.2), and low adherence to ART treatment (AHR = 4.2, 95% CI: 2.5-7.1). Conclusion: An effective reduction in HIV/AIDS mortality could be achieved through timely ART treatment onset and maintaining high levels of treatment adherence.

Modeling time-to-cure from severe acute malnutrition: application of various parametric frailty models.

BACKGROUND: 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. RESULTS: 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.