<div dir="ltr"><h3><span style="font-weight:normal;font-size:small">Boa noite todos,</span></h3><div><h3><font size="2"><span style="font-weight:normal">Esta mensagem é para divulgar mais uma palestra do Ciclo de <span style="color:rgb(60,64,67);background-color:rgb(253,226,147)"><span>Seminários</span></span> do <span>PPGE</span>/UFPE (2023), que será realizada na quarta-feira 21/06, às 15:00 de forma remota.</span></font></h3></div><div><h3><span style="color:rgb(60,64,67);font-family:Roboto,Arial,sans-serif;font-size:14px;letter-spacing:0.2px">Palestrante: </span><span style="color:rgb(60,64,67);font-family:Roboto,Arial,sans-serif;font-size:14px;font-weight:400;letter-spacing:0.2px">Agatha Sacramento Rodrigues - UFES</span></h3><h3><div style="font-size:small">Sobre a palestra:</div><span style="font-weight:normal">Link da videochamada: <a href="https://meet.google.com/kvp-wpff-koi">https://meet.google.com/kvp-wpff-koi</a><br>Ou disque: ‪(US) +1 567-218-3295‬ PIN: ‪299 626 538‬#</span><br></h3><h3><br style="color:rgb(60,64,67);font-family:Roboto,Arial,sans-serif;font-size:14px;font-weight:400;letter-spacing:0.2px"><p class="MsoNormal" style="margin:0cm 0cm 8pt;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial;line-height:16.8667px;font-size:11pt;font-family:Calibri,"sans-serif""><span lang="EN-US" style="">Título: <span style="font-weight:400">A defective cure rate quantile regression model for a maternal population with severe COVID-19</span></span></p><p class="MsoNormal" style="margin:0cm 0cm 8pt;font-weight:400;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial;line-height:16.8667px;font-size:11pt;font-family:Calibri,"sans-serif""><span lang="EN-US"> </span></p><p class="MsoNormal" style="margin:0cm 0cm 8pt;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial;line-height:16.8667px;font-size:11pt;font-family:Calibri,"sans-serif""><span lang="EN-US" style="">Resumo:</span></p><p class="MsoNormal" style="margin:0cm 0cm 8pt;font-weight:400;text-align:justify;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial;line-height:16.8667px;font-size:11pt;font-family:Calibri,"sans-serif""><span lang="EN-US">In this work, we will particularly address the problem of assessing prognostic factors 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 in 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 Gompertz distribution 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. This 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.</span></p><p class="MsoNormal" style="margin:0cm 0cm 8pt;font-weight:400;text-align:justify;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial;line-height:16.8667px;font-size:11pt;font-family:Calibri,"sans-serif""><span lang="EN-US"> </span></p><p class="MsoNormal" style="margin:0cm 0cm 8pt;font-weight:400;text-align:justify;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial;line-height:16.8667px;font-size:11pt;font-family:Calibri,"sans-serif""><span lang="EN-US">Trabalho conjunto com Patrick Borges (Departamento de Estatística da Universidade Federal do Espírito Santo) and Bruno Santos (School of Mathematics, Statistics, and Actuarial Science -  University of Kent, Canterbury, Reino Unido)</span></p></h3></div><div><p style="line-height:1.75;margin-top:11.25pt;margin-bottom:0pt">Contamos com a presença de todos!</p><p style="line-height:1.75;margin-top:11.25pt;margin-bottom:0pt">Att,</p></div><div><br></div><span class="gmail_signature_prefix">-- </span><br><div dir="ltr" class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div><div dir="ltr">Maria do Carmo Soares de Lima<div>Professora Adjunta C- UFPE</div></div></div></div></div></div>