A tailored fit that doesn’t fit all: the problem of threshold overfitting in diagnostic studies

Abstract

OBJECTIVES: To critically examine the phenomenon of threshold overfitting in diagnostic accuracy research and evaluate its methodological implications through a structured review of relevant literature. METHODS: thresholds, the misapplication of bias assessment tools such as QUADAS-2, and the frequent absence of independent validation. In addition to identifying these structural flaws, the article proposes a set of five concrete safeguards - ranging from transparent reporting to rigorous risk of bias classification - designed to mitigate threshold-related bias in future diagnostic studies. RESULTS: Thresholds are frequently derived and evaluated within the same dataset, inflating sensitivity and specificity estimates. This overfitting is seldom acknowledged and is often misclassified as low risk of bias. QUADAS-2 is frequently misapplied, with reviewers mistaking the mere presence of a threshold for proper pre-specification. The article identifies five key safeguards to mitigate this bias: (1) clear declaration of pre-specification, (2) justification of threshold choice, (3) independent validation, (4) full performance reporting across thresholds, and (5) rigorous application of bias assessment tools. CONCLUSIONS: Threshold overfitting remains an underrecognized but methodologically critical source of bias in diagnostic accuracy studies. Addressing it requires more than awareness - it demands transparent reporting, proper validation, and stricter adherence to methodological standards.

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