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has been cited by the following article:

• TITLE: On the Survival Assessment of Asthmatic Patients Using Parametric and Semi-Parametric Survival Models

  AUTHORS: Nureni Olawale Adeboye, Ilesanmi A. Ajibode, Olubisi L. Aako

  KEYWORDS: Asthma Disease, Cox Regression Model, Hazard Rates, Parametric Regression Models, Prognostic Factors

  JOURNAL NAME: Occupational Diseases and Environmental Medicine, Vol.8 No.2, April 14, 2020

  ABSTRACT: The goals of asthma management are to prevent or minimize symptoms, avert or reduce risk of asthma attacks and to ensure that asthma does not limit the patient’s activities since it is not curable. Thus in this study, the degrees of success following treatments given to patients over time were assessed based on the patient’s length of stay on admission and factors responsible for patients’ response to treatment were equally examined using survival analysis models of parametric and semi-parametric distributions. The study was conducted on 464 asthmatic patients from four different hospitals in Ogun State. The data were extracted from patients’ records and prognostic factors such as age, sex, smoking, hereditary, obesity, respiratory illness and environmental pollution were considered for survival analysis. It was observed that there was drastic reduction in survival rate from 7 days upward at a cut-off probability value of 0.485, based on Kaplan-Meier (KM) results. Log-normal regression model, a parametric model with the least AIC value (2969.74) and least negative Log likelihood value (1475.87) shows best performance in handling asthma data with prognostic factors of Smoking (HR = 1.32, 95% CI: 0.93 - 1.88), Obesity (HR = 1.25, 95% CI: 0.80 - 1.93), Environmental pollution (HR = 0.79, 95% CI: 0.52 - 1.18) and Respiratory illness (HR = 1.93, 95% CI: 1.33 - 2.79) were found to have significantly affected the length of stay of asthmatic patients in hospital.