The Undertesting Bias in Clinical Decision Making: The Role of Test Dichotomization

Threshold models, formalized by Pauker and Kassirer, guide clinical decisions about testing and treatment by partitioning pretest probabilities into three action zones using two threshold pretest probabilities: the test threshold and the test–treatment threshold. This derivation implicitly assumes that diagnostic tests possess fixed sensitivity and specificity, effectively treating them as binary predictors. However, many diagnostic tests yield continuous or ordinal outputs, in which case the optimal sensitivity–specificity pair varies with the pretest probability. I demonstrate that abstracting from this dependency leads to an undertesting bias: the testing threshold is inflated and/or the test–treatment threshold is deflated, resulting in a narrower than optimal testing window. This bias systematically undervalues continuous diagnostic tests and leads to their underuse. Clinical decision-making models and guidelines should therefore recognize that optimal test score cutoffs depend on pretest probabilities to avoid this systematic underuse of diagnostic testing.