Pires Magda Carvalho, Colosimo Enrico Antônio, Veloso Guilherme Augusto, Ferreira Raquel de Souza Borges
Departamento de Estatística, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
J Appl Stat. 2020 Apr 16;48(5):907-923. doi: 10.1080/02664763.2020.1753025. eCollection 2021.
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 compared to the scenario with no validation subset or the model that assumes perfect sensitivity and specificity. Finally, we illustrate the usefulness of the new methodology with an analysis of real data about HIV acquisition in female sex workers that have been discussed in the literature.
涉及无症状事件的生存数据如果应用了灵敏度和特异度并非完美的检测，往往会受到区间删失（已知事件在一个时间间隔内发生）和分类错误的影响。考虑此类数据的性质对于估计事件发生前的时间分布起着重要作用。在此背景下，我们将验证子集纳入参数化比例风险模型，并表明这些额外的数据与贝叶斯推断相结合，能够弥补检测灵敏度和特异度方面的知识不足，从而改进参数估计。通过模拟研究对所提出的模型进行评估，并在吉布斯抽样程序中进行贝叶斯分析。与没有验证子集的情形或假设灵敏度和特异度完美的模型相比，在验证子集模型下获得的后验估计偏差和标准差更低。最后，我们通过分析文献中讨论的女性性工作者感染艾滋病毒的真实数据来说明新方法的实用性。
