Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter October 21, 2011

Nonparametric and Semiparametric Analysis of Current Status Data Subject to Outcome Misclassification

Victor G. Sal y Rosas and James P. Hughes

In this article, we present nonparametric and semiparametric methods to analyze current status data subject to outcome misclassification. Our methods use nonparametric maximum likelihood estimation (NPMLE) to estimate the distribution function of the failure time when sensitivity and specificity are known and may vary among subgroups. A nonparametric test is proposed for the two sample hypothesis testing. In regression analysis, we apply the Cox proportional hazard model and likelihood ratio based confidence intervals for the regression coefficients are proposed. Our methods are motivated and demonstrated by data collected from an infectious disease study in Seattle, WA.

Published Online: 2011-10-21

©2012 Walter de Gruyter GmbH & Co. KG, Berlin/Boston