Parametric Survival Modeling of Tuberculosis Data- A Case Study of Federal Medical Centre, Bida, Nigeria

Vincent Abiodun Michael and Ismaila Adewale Bolarinwa

Modern Applied Science, 2020, vol. 14, issue 7, 37

Abstract: The research performed parametric survival analysis of Tuberculosis (TB) data (covering 2010 to 2016) collected from the Federal Medical Centre, Bida, Niger State, Nigeria. Three parametric survival models (Exponential, Weibull and Log-logistic) were fitted. The outcome variable was time to recovery from TB infection and four covariates being age, gender, TB type and occupation were involved. Models were estimated by maximum likelihood method and model selection criterion used was the Akaike Information Criterion (AIC). The exponential and log-logistic models found all covariates statistically insignificant while Weibull found all covariates but TB type significant at 5% level. Based on AIC, Weibull model with AIC of 163.5731 performed best, followed by log-logistic model with AIC of 191.419 and exponential model performed worst, with AIC of 517.9652. The best of fitted models being Weibull suggested that older patients had higher hazards than younger ones, older patients hence, had lower survival times, holding other covariates constant. That is, the older the TB patient, the lower was the time to recovery from TB. Males had higher hazards and hence, lower survival times compared to females. That is, male TB patients recovered faster than the females. Pulmonary TB patients had lower (insignificant) hazards and hence, higher survival times than Respiratory TB patients. TB patients on technical occupation had lower hazards than others and hence, had higher survival times than those whose occupations were considered not technical. The research concluded that age, gender and occupation were the major determinants of recovery period of TB patients. It was recommended that the Management of Federal Medical Centre, Bida, and other organizations involved in TB management could make use of the Weibull model to fit and predict both the survival and hazard rates of TB patients.

Date: 2020
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