Confidence Intervals for a Proportion Using Inverse Sampling when the Data is Subject to False-positive Misclassification

Abstract

Of interest in this paper is the development of a model that uses 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 score and likelihood ratio intervals are generally preferable over the Wald interval. Lastly, the model is applied to a medical data set.