An Extended Weibull Regression for Censored Data: Application for COVID-19 in Campinas, Brazil

This work aims to study the factors that increase the risk of death of hospitalized patients diagnosed with COVID-19 through the odd log-logistic regression model for censored data with two systematic components, as well as provide new mathematical properties of this distribution. To achieve this, a dataset of individuals residing in the city of Campinas (Brazil) was used and simulations were performed to investigate the accuracy of the maximum likelihood estimators in the proposed regression model. The provided properties, such as stochastic representation, identifiability, and moments, among others, can help future research since they provide important information about the distribution structure. The simulation results revealed the consistency of the estimates for different censoring percentages and show that the empirical distribution of the modified deviance residuals converge to the standard normal distribution. The proposed model proved to be efficient in identifying the determinant variables for the survival of the individuals in this study, which can help to find more opportune treatments and medical interventions. Therefore, the new model can be considered an interesting alternative for future works that evaluate censored lifetimes.

Automated summary

This study examines the factors that increase the risk of death in hospitalized patients with COVID-19 using the odd log-logistic regression model and provides new mathematical properties of this distribution. The simulation results demonstrate the consistency of the estimates and suggest that the proposed model is efficient in identifying the determinant variables for individual survival.