Calibration of clinical prediction rules does not just assess bias.

Abstract:

OBJECTIVES:Calibration is often thought to assess the bias of a clinical prediction rule. In particular, if the rule is based on a linear logistic model, it is often assumed that an overestimation of all coefficients results in a calibration slope less than 1 and an underestimation in a slope larger than 1. STUDY DESIGN AND SETTING:We investigate the relation of the bias and the residual variation of clinical prediction rules with the typical behavior of calibration plots and calibration slopes, using some artificial examples. RESULTS:Calibration is not only sensitive to the bias of the clinical prediction rule but also to the residual variation. In some circumstances, the effects may cancel out, resulting in a misleading perfect calibration. CONCLUSION:Poor calibration is a clear indication of limited usefulness of a clinical prediction rule. However, a perfect calibration should be interpreted with care as this may happen even for a biased prediction rule.

journal_name

J Clin Epidemiol

authors

Vach W

doi

10.1016/j.jclinepi.2013.06.003

subject

Has Abstract

pub_date

2013-11-01 00:00:00

pages

1296-301

issue

11

eissn

0895-4356

issn

1878-5921

pii

S0895-4356(13)00237-0

journal_volume

66

pub_type

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