Parametric Survival Modelling of Risk Factor of Tuberculosis Patients under DOTS Program at Hawassa Town, Ethiopia

Tuberculosis (TB) is a chronic infectious disease that has a major health problem over the centuries. This study assessed the risk factors associated with time to death among TB patients treated under directly observed short course treatment program in Health facilities in Hawassa city, Ethiopia. The authors analysed data from a cohort of 1604 TB patients recruited between September 2008 to September 2011. They apply the parametric regression model of survival data analysis. The best fitted parametric regression model is selected by using the Akaike information criterion (AIC). The AIC confirms that the Weibull regression model is found to be the best fit of the survival of tuberculosis patients under the DOTS program at Hawassa town, Ethiopia. The fit of the Weibull regression model result revealed that sex, age, baseline weight, HIV status, category of patients and year of enrolment are the significant factor for the survival of TB patients.

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Parametric regression model for survival data: Weibull regression model as an example

Annals of Translational Medicine ◽

10.21037/atm.2016.08.45 ◽

2016 ◽

Vol 4 (24) ◽

pp. 484-484 ◽

Cited By ~ 26

Author(s):

Zhongheng Zhang

Keyword(s):

Regression Model ◽

Survival Data ◽

Weibull Regression ◽

Parametric Regression ◽

Weibull Regression Model

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Confidence Bands for the Survival Function Using a Weibull Regression Model in Presence of Arbitrary Censoring

Revista Colombiana de Estadística ◽

10.15446/rce.v40n1.55807 ◽

2017 ◽

Vol 40 (1) ◽

pp. 85-103

Author(s):

Mario César Jaramilo Elorza ◽

Juan Carlos Salazar Uribe

Keyword(s):

Regression Model ◽

Survival Data ◽

Survival Function ◽

Interval Censoring ◽

Confidence Bands ◽

Positive Development ◽

Weibull Regression ◽

Elapsed Time ◽

Initial Event ◽

Weibull Regression Model

Usually, the exact time at which an event occurs cannot be observed for several reasons; for instance, it is not possible to constantly monitor a characteristic of interest. This generates a phenomenon known as censoring that can be classified as having a left censor, right censor or interval censor. When one is working with survival data in the presence of arbitrary censoring, the survival time of interest is defined as the elapsed time between an initial event and the next event that is generally unknown. This problem has been widely studied in the statistic literature and some progress has been made, toward resolving and the formulation of a bivariate likelihood to estimate parameters in a parametric regression model offers positive development opportunities. In this paper, we construct a bivariate likelihood for the Weibull regression model in the presence of interval censoring. Finally, its performance is illustrated by means of a simulation study.

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Sustainability Time and Its Determinant Factors of Small and Medium-scale Enterprises in East Gojjam Zone: Parametric Survival Regression Model

Global Business Review ◽

10.1177/0972150920988657 ◽

2021 ◽

pp. 097215092098865

Author(s):

Amare Wubishet Ayele ◽

Abebaw Bizuayehu Derseh

Keyword(s):

Regression Model ◽

Marketing Channel ◽

Educational Status ◽

Weibull Regression ◽

Service Sectors ◽

Weibull Regression Model ◽

Parametric Survival ◽

Prospective Study Design ◽

Significant Factors ◽

Survival Regression

The contributions of small and medium-sized enterprises (SMEs) to socio-economic development are generally recognized, but they have faced several obstacles that impede their sustainability. This manuscript seeks to identify factors for the survival of SMEs in the East Gojjam Zone, Ethiopia. The prospective study design was employed. Both descriptive and inferential statistics, particularly families of parametric survival regression models, have been used. Of the 650 enterprises included in this study, 330 (50.8%) were censored (sustained enterprises) and the remaining 320 (49.2%) were events or withdrawn enterprises. The findings of this study revealed that the incidence of termination or withdrawal of SMEs in the study area is relatively common. The results from multivariable Weibull regression model revealed that woreda, sector, manger profile (gender, age, educational status, experience (in year) and source of experience), working place, marketing channel and profitability district status of enterprise were found to be statistically significant factors for the sustainability of enterprises in the study area. The bodies concerned, in particular the enterprise administrative offices at various levels, should work with collaborative organizations to develop a strong marketing platform (network), should be able to make workplaces accessible with the required infrastructure at minimal rental costs, and should prioritize the type of sector that has the highest customer needs at the onset, for instance, agriculture and service sectors.

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