Article ID Journal Published Year Pages File Type
1153558 Statistical Methodology 2011 9 Pages PDF
Abstract

We develop likelihood-based confidence intervals for risk difference in two-sample misclassified binary data. Such data consist of two studies. The first study is the main study where individuals are classified by an inexpensive fallible classifier which may misclassify. The second study is a validation substudy where individuals are classified by using both the fallible classifier and an expensive gold standard which classifies perfectly. We propose and examine three likelihood-based confidence interval methods and conclude that the modified Wald method applied to small-number adjusted new data performs well and has nominal coverage probabilities.

Related Topics
Physical Sciences and Engineering Mathematics Statistics and Probability
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