Marginal Bayesian Semiparametric Modeling of Mismeasured Multivariate Interval-Censored Data

ABSTRACT

Motivated by data gathered in an oral health study, we propose a Bayesian nonparametric approach for population-averaged modeling of correlated time-to-event data, when the responses can only be determined to lie in an interval obtained from a sequence of examination times and the determination of the occurrence of the event is subject to misclassification. The joint model for the true, unobserved time-to-event data is defined semiparametrically; proportional hazards, proportional odds, and accelerated failure time (proportional quantiles) are all fit and compared. The baseline distribution is modeled as a flexible tailfree prior. The joint model is completed by considering a parametric copula function. A general misclassification model is discussed in detail, considering the possibility that different examiners were involved in the assessment of the occurrence of the events for a given subject across time. We provide empirical evidence that the model can be used to estimate the underlying time-to-event distribution and the misclassification parameters without any external information about the latter parameters. We also illustrate the effect on the statistical inferences of neglecting the presence of misclassification. Supplementary materials for this article are available online.

KEYWORDS:

• Copula function
• Mismeasured continuous response
• Multivariate survival data
• Population-averaged modeling

Additional information

Funding

The research time of Li was supported in part by the National Cancer Institute grant (5P30CA118100-11; the National Cancer Institute, USA; PI: Willman). The second author was supported by Fondecyt 1141193 and 1180640 grants. The third author was supported by Fondecyt 11110033 grant. The work was partially performed during a visit of the fourth author to Pontificia Universidad Católica de Chile, supported by Fondecyt 11110033 grant. M. J. García-Zattera and A. Jara were also partially supported by Iniciativa Científica Milenio - Minecon Núcleo Milenio MIDAS.