Regression Analysis of Misclassified Current Status Data with Potentially Unknown Test Accuracy

Current status data are frequently encountered in many real life cross-sectional epidemiological, demographic, and medical studies, where each subject is examined only once, and the failure time of interest is never exactly observed but known to be either smaller or larger than the examination time for each subject by evaluating the failure status. Consequently, current status data are a mixture of left-censored and right-censored observations for the failure times of all subjects with or without covariates. In some real life studies, the test or diagnosis that is used to determine the failure status may be error-prone, and this leads to misclassified failure status for some or all subjects. The resulting data are referred to as misclassified current status data in the literature. In this paper, we study regression analysis of misclassified current status data and propose a novel estimation approach under the proportional odds model. Specifically, monotone splines are adopted to approximate the baseline odds function, and an efficient expectation-maximization algorithm is developed based on a data augmentation involving exponential and Poisson latent variables. An extension of the proposed method is also developed to account for the unknown test accuracy. The proposed method is shown to have excellent estimation performance in our simulation studies and is illustrated by an application to uterine fibroid data.