Survival Analysis Among Tuberculosis Patients: A Case Study of Adults in Kano State in Nigeria
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Date
2022-05
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Publisher
University Of Ghana
Abstract
Tuberculosis (TB) is an infectious disease that has been considered as a signi_-
cant risk factor that causes ill health. Globally, it has been found to be among
the top 10 causes of death and ranks above HIV/AIDS as a single infectious agent
that causes death in patient. Many researches have been documented using semiparametric
and non-parametric models to analyze survival data in Nigeria. There
is dearth of studies on the use of parametric models on tuberculosis survival data.
Parametric models such as Weibull, Exponential, Log-logistic, Gompertz etc have
been used in various studies to analyze data and Weibull was mostly found to be
suitable. The popular non-parametric and semi-parametric tests used in various
studies include the K-M, Log rank and Cox Proportional hazard model. However,
necessary diagnostic checks on model _tness and non-violation of assumptions
were mostly ignored. This reduces the reliability of result and increase chance of
estimation error. This study assessed the parametric and semi-parametric model
of survival such as Cox Model, Weibull, Exponential and Gompertz Models. A
retrospective cohort analysis was conducted on the tuberculosis patients receiving
treatment under the Tuberculosis & Leprosy Control Program in Kano, Nigeria.
The risk factors for death were assessed using the Cox proportional hazard model.
The risk factors for death were assessed using the Cox proportional hazard model.
The parametric models were compared, and the gompertz model was found to
be the best _t for the data based on its minimum AIC & log-likelihood value.
Among 2,555 the TB cases, the success rate of TB treatment was 97.06% and
the mortality rate was 2.94%. Multivariate analysis showed that HIV, Age &
Weight were signi_cant factors associated with mortality in TB patients during
therapy. The study recommends the use of diagnostic checks such as Martingale,
Deviance Residuals in model _tness. Also, comparism of parametric models
is recommended in determination of best model that _ts tuberculosis data of
patients.
Key words: Survival Analysis, Kaplan Meier, Cox Proportional Hazard Model,
Parametric Models, Tuberculosis.
Description
MPhil. Actuarial Science
Keywords
Survival Analysis, Kaplan Meier, Cox Proportional Hazard Model