Classification accuracy goals for diagnostic tests based on risk stratification

Abstract

In diagnostic test evaluation, performance goals are often set for classification accuracy measures such as specificity, sensitivity and diagnostic likelihood ratio. For tests that detect rare conditions, classification accuracy goals are attractive because they can be evaluated in case-control studies enriched for the condition. A neglected area of research is determining classification accuracy goals that confer clinical usefulness of a test. We determine classification accuracy goals based on desired risk stratification, i.e. the post-test risk of having the condition compared with the pre-test risk. We determine goals for rule-out tests, rule-in tests, and those that do both. Goals for negative and positive likelihood ratios (NLR, PLR) are emphasized because of their natural relationships with risk stratification via Bayes Theorem. Goals for specificity and sensitivity are implied by goals on NLR and PLR. Goals that confer superiority or non-inferiority of a test to a comparator are based on approximating risk differences and relative risks by functions of likelihood ratios. Inference is based on Wald confidence intervals for ratios of likelihood ratios. To illustrate, we consider hypothetical data on a fetal fibronectin assay for ruling out risk of pre-term birth and two human papillomavirus assays for detecting cervical cancer. Trial registration ClinicalTrials.gov identifier: NCT01931566.