Normal Theory And Bootstrap Confidence Intervalestimation In Assessing Diagnostic Performance gain When Combining Two Diagnostic Tests

摘要

Many diagnostic tests may be available to identify a particular disease. Diagnostic performance can be potentially improved by combining. "Either" and "both" positive strategies for combining tests have been discussed in the literature, where a gain in diagnostic performance is measured by a ratio of positive (negative) likelihood ratio of the combined test to that of an individual test. Normal theory and bootstrap confidence intervals are constructed for gains in likelihood ratios. The performance (coverage probability, width) of the two methods are compared via simulation. All confidence intervals perform satisfactorily for large samples, while bootstrap performs better in smaller samples in terms of coverage and width.