Name: Guilherme Augusto Veloso
Institution: Departamento de Estatística, Universidade Federal de Minas Gerais, Minas Gerais, Brazil
E-mail: guilerme-kz-@hotmail.com
Co-authors: Magda Carvalho Pires, Enrico Antônio Colosimo, Raquel de Souza Borges Ferreira
Abstract:
Survival data involving silent events are often subject to interval censoring (the event is known to occur within a time interval) and classification errors if a test with no perfect sensitivity and specificity is applied. Considering the nature of this data plays an important role in estimating the time distribution until the occurrence of the event. In this context, we incorporate validation subsets into the parametric proportional hazard model, and show that this additional data, combined with Bayesian inference, compensate the lack of knowledge about test sensitivity and specificity improving the parameter estimates. The proposed model is evaluated through simulation studies, and Bayesian analysis is conducted within a Gibbs sampling procedure. The posterior estimates obtained under validation subset models present lower bias and standard deviation. Finally, we illustrate the usefulness of the new methodology with an analysis of real data about HIV acquisition in female sex workers that has been discussed in the literature.
Keywords: Bayesian inference, interval-censored data, misclassification, survival analysis, validation subset