Confidence intervals for a proportion using a fixed-inverse double sampling scheme when the data are subject to false-positive misclassification

Abstract

Of interest in this paper is the development of a model that uses fixed, then inverse sampling of binary data that is subject to false-positive misclassification in an effort to estimate a proportion. From this model, both the proportion of success and false-positive misclassification rate may be estimated. Also, three first-order likelihood-based confidence intervals for the proportion of success are mathematically derived and studied via a Monte Carlo simulation. The simulation results indicate that the likelihood ratio interval is generally preferable over the Wald and score interval. Lastly, the model is applied to two different real-world medical data sets.

KEYWORDS:

• Misclassification
• Double sampling
• Negative multinomial sampling
• Interval estimation
• Likelihood methods

Acknowledgements

This work was supported, in part, by a research grant from the Science Technology Engineering Mathematics (STEM) Center at Stephen F. Austin State University.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported, in part, by a research grant from the Science Technology Engineering Mathematics (STEM) Center at Stephen F. Austin State University.