Likelihood ratios with confidence: sample size estimation for diagnostic test studies.

Confidence intervals are important summary measures that provide useful information from clinical investigations, especially when comparing data from different populations or sites. Studies of a diagnostic test should include both point estimates and confidence intervals for the tests' sensitivity and specificity. Equally important measures of a test's efficiency are likelihood ratios at each test outcome level. We present a method for calculating likelihood ratio confidence intervals for tests that have positive or negative results, tests with non-positive/non-negative results, and tests reported on an ordinal outcome scale. In addition, we demonstrate a sample size estimation procedure for diagnostic test studies based on the desired likelihood ratio confidence interval. The renewed interest in confidence intervals in the medical literature is important, and should be extended to studies analyzing diagnostic tests.

Duke Scholars

Author David Lee Simel Medicine, General Internal Medicine

Author Gregory P. Samsa Biostatistics & Bioinformatics, Division of Biostatistics

Author David Bruce Matchar Medicine, General Internal Medicine