Format

Send to

Choose Destination
Med Decis Making. 2015 Feb;35(2):162-9. doi: 10.1177/0272989X14547233. Epub 2014 Aug 25.

Calibration of risk prediction models: impact on decision-analytic performance.

Author information

1
KU Leuven, Department of Development and Regeneration, Leuven, Belgium (BVC)
2
Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY (AJV).

Abstract

Decision-analytic measures to assess clinical utility of prediction models and diagnostic tests incorporate the relative clinical consequences of true and false positives without the need for external information such as monetary costs. Net Benefit is a commonly used metric that weights the relative consequences in terms of the risk threshold at which a patient would opt for treatment. Theoretical results demonstrate that clinical utility is affected by a model';s calibration, the extent to which estimated risks correspond to observed event rates. We analyzed the effects of different types of miscalibration on Net Benefit and investigated whether and under what circumstances miscalibration can make a model clinically harmful. Clinical harm is defined as a lower Net Benefit compared with classifying all patients as positive or negative by default. We used simulated data to investigate the effect of overestimation, underestimation, overfitting (estimated risks too extreme), and underfitting (estimated risks too close to baseline risk) on Net Benefit for different choices of the risk threshold. In accordance with theory, we observed that miscalibration always reduced Net Benefit. Harm was sometimes observed when models underestimated risk at a threshold below the event rate (as in underestimation and overfitting) or overestimated risk at a threshold above event rate (as in overestimation and overfitting). Underfitting never resulted in a harmful model. The impact of miscalibration decreased with increasing discrimination. Net Benefit was less sensitive to miscalibration for risk thresholds close to the event rate than for other thresholds. We illustrate these findings with examples from the literature and with a case study on testicular cancer diagnosis. Our findings strengthen the importance of obtaining calibrated risk models.

KEYWORDS:

calibration; decision curve analysis; decision-analytic measures; discrimination; net benefit; prediction models; risk scores

PMID:
25155798
DOI:
10.1177/0272989X14547233
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Atypon
Loading ...
Support Center