Misclassification of current status data.
We describe a simple method for nonparametric estimation of a distribution function based on current status data where observations of current status information are subject to misclassification. Nonparametric maximum likelihood techniques lead to use of a straightforward set of adjustments to the familiar pool-adjacent-violators estimator used when misclassification is assumed absent. The methods consider alternative misclassification models and are extended to regression models for the underlying survival time. The ideas are motivated by and applied to an example on human papilloma virus (HPV) infection status of a sample of women examined in San Francisco.
McKeown
Karen
K
Division of Biostatistics, School of Public Health, University of California, 101 Haviland Hall MC 7358, Berkeley, CA, 94720, USA. karen.mckeown@berkeley.edu
Jewell
Nicholas P
NP
D000328:Adult; D000368:Aged; D002965:Classification; D018450:Disease Progression; D005260:Female; D015658:HIV Infections; D006801:Humans; D008875:Middle Aged; D030361:Papillomavirus Infections; D012044:Regression Analysis; D012495:San Francisco; D016019:Survival Analysis