Abstract
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 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.
Disciplines
Statistical Models
Suggested Citation
Sal y Rosas, Victor G. and Hughes, James P., "Nonparametric and Semiparametric Analysis of Current Status Data Subject to Outcome Misclassification" (April 2010). UW Biostatistics Working Paper Series. Working Paper 364.
https://biostats.bepress.com/uwbiostat/paper364