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Title: A defective cure rate quantile regression model for a maternal population with severe COVID-19 Authors:  Agatha Rodrigues - Universidade Federal do Espirito Santo (Brazil) [presenting]
Patrick Borges - Federal University of Espirito Santo (Brazil)
Bruno Santos - University of Kent (United Kingdom)
Abstract: The aim is to address the problem of assessing the age and ethnicity on the specific survival times of pregnant and postpartum women hospitalized with severe acute respiratory syndrome confirmed by COVID-19 when cure is a possibility, where there is also the interest of explaining this impact on different quantiles of the survival times. To this end, we fitted a quantile regression model for survival data in the presence of long-term survivors based on the generalized distribution of Gompertz in a defective version, which is conveniently reparametrized in terms of the $q$-th quantile and then linked to covariates via a logarithm link function. The considered approach allows us to obtain how each variable affects the survival times in different quantiles. In addition, we are able to study the effects of covariates on the cure rate as well. We consider Markov Chain Monte Carlo (MCMC) methods to develop a Bayesian analysis in the proposed model and we evaluate its performance through a Monte Carlo simulation study. The study is part of the Brazilian Obstetric Observatory, a multidisciplinary project that aims to monitor and analyze public data from Brazil in order to disseminate relevant information in the area of maternal and child health.