TY - JOUR
AU - J. Dowie
AU - M. K. Kaltoft
AU - V. K. Rajput
A1 - 
AB - INTRODUCTION: Ordinal tests, an important source of information on mental disorders in primary care, are validated by their 'diagnostic accuracy' in relation to a binary reference standard. Their Sensitivities and Specificities are measured at each possible cutoff on the test scale and the 'optimal' cutoff determined as that which maximises the separation of healthy from unhealthy populations, usually by Youden's statistic. This gives equal weight to Sensitivity and Specificity, which, while valid from this population research perspective, has no relevance in clinical practice (screening, case-finding). Use of an 'optimal' cutoff breaches the requirement for the patient to be provided with the most individualised test output metrics possible, in order that they may bring their informed preferences to bear in the post-test decision making. METHOD: The magnitude of the optimality bias is investigated empirically using the DEPRESSD Individual Participant dataset-based meta-analyses for the Edinburgh Postnatal Depression Scale and the Patient Health Questionnaire 9. RESULT: An online Clinical Information Aid produced from these meta-analyses makes instantly accessible a full set of test metric outputs at all cutoffs and crucially that at the patient's precise score cutoff. CONCLUSION: The optimality bias can be both established and eliminated.
AD - London School of Hygiene and Tropical Medicine, London, UK.; University of Southern Denmark, Odense, Denmark.; Keele University, Keele, UK.
AN - 42175346
BT - Stud Health Technol Inform
C5 - Measures
DA - 05/2026
DO - 10.3233/shti260679
DP - NLM
JF - Stud Health Technol Inform
LA - eng
N2 - INTRODUCTION: Ordinal tests, an important source of information on mental disorders in primary care, are validated by their 'diagnostic accuracy' in relation to a binary reference standard. Their Sensitivities and Specificities are measured at each possible cutoff on the test scale and the 'optimal' cutoff determined as that which maximises the separation of healthy from unhealthy populations, usually by Youden's statistic. This gives equal weight to Sensitivity and Specificity, which, while valid from this population research perspective, has no relevance in clinical practice (screening, case-finding). Use of an 'optimal' cutoff breaches the requirement for the patient to be provided with the most individualised test output metrics possible, in order that they may bring their informed preferences to bear in the post-test decision making. METHOD: The magnitude of the optimality bias is investigated empirically using the DEPRESSD Individual Participant dataset-based meta-analyses for the Edinburgh Postnatal Depression Scale and the Patient Health Questionnaire 9. RESULT: An online Clinical Information Aid produced from these meta-analyses makes instantly accessible a full set of test metric outputs at all cutoffs and crucially that at the patient's precise score cutoff. CONCLUSION: The optimality bias can be both established and eliminated.
PY - 2026
SN - 0926-9630
SP - 2294
EP - 2298+
ST - Addressing the 'optimal cutoff' Bias in Primary Care Testing
T1 - Addressing the 'optimal cutoff' Bias in Primary Care Testing
T2 - Stud Health Technol Inform
TI - Addressing the 'optimal cutoff' Bias in Primary Care Testing
U1 - Measures
U3 - 10.3233/shti260679
VL - 336
VO - 0926-9630
Y1 - 2026
ER -