Bias in Sensitivity and Specificity Caused by Data-Driven Selection of Optimal Cutoff Values: Mechanisms, Magnitude, and Solutions | Zendy

Mariska Leeflang | Zendy; Karel G.M. Moons | Zendy; Johannes B. Reitsma | Zendy; A.H. Zwinderman | Zendy

Open Access

Bias in Sensitivity and Specificity Caused by Data-Driven Selection of Optimal Cutoff Values: Mechanisms, Magnitude, and Solutions

Author(s) -

Mariska Leeflang,

Karel G.M. Moons,

Johannes B. Reitsma,

A.H. Zwinderman

Publication year - 2008

Publication title -

clinical chemistry

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.705

H-Index - 218

eISSN - 1530-8561

pISSN - 0009-9147

DOI - 10.1373/clinchem.2007.096032

Subject(s) - cutoff , sample size determination , statistics , sensitivity (control systems) , magnitude (astronomy) , youden's j statistic , selection bias , mathematics , sample (material) , sampling bias , selection (genetic algorithm) , receiver operating characteristic , econometrics , computer science , physics , quantum mechanics , astronomy , electronic engineering , artificial intelligence , engineering , thermodynamics

Optimal cutoff values for tests results involving continuous variables are often derived in a data-driven way. This approach, however, may lead to overly optimistic measures of diagnostic accuracy. We evaluated the magnitude of the bias in sensitivity and specificity associated with data-driven selection of cutoff values and examined potential solutions to reduce this bias.

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