COMPARISON OF COX’S AND WEIBULL REGRESSION MODELS IN ASSESSING THE PROGNOSTIC FACTORS FOR SURVIVAL OF ASTHMATIC PATIENTS

Author: 
Ezekiel, ImekelaDonaldson and Aako, Olubisi Lawrence
Country: 
Nigeria
Abstract: 

Survival analysis is a branch of statistics for analyzing the expected duration of time until one or more events happen. This study aimed to compare the results of Cox Proportional hazards model and Weibull Regression model to determine the model that best fits for Asthma data and observe factors that affect asthma patient’s length of stay in hospital. Kaplan-Meier (K-M) method was used to estimate the survival rates of patients and to plot the graph of survival curves using data obtained from Federal Medical Centre, Abeokuta, Nigeria on Asthma patients. In using the K-M method, it was observed that there was low survival rate from 14 days upward. Akaike Information Criterion (AIC) and Log likelihood methods were used to evaluate the two models. Weibull Regression Model had the least AIC value of 428.3163 with highest Log likelihood value of -205.2 which shows best performance in handling Asthma data as compared to Cox Regression Model with highest AIC value 485.2536 and least Log likelihood value of -235.63.The result of the study showed that the parametric Weibull Regression Model could better determine the factors associated with the Asthma disease than the semi-parametric Cox Proportional hazards model. Determinant factors such as sex, smoking, hereditary, obesity, environmental pollution and respiratory illness were found to be significant factors affecting the length of stay in hospital of Asthma patients.

Volume & Issue: 
Vol. 4, Issue, 11
Pages: 
1390-1394
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