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.
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.
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.
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Published online: September 10, 2013
Accepted: June 2, 2013
Conflict of interest: This material has neither been published nor is under consideration for publication elsewhere. There is no external funding involved in this study, and there are no possible conflicts of interest.
© 2013 Elsevier Inc. Published by Elsevier Inc. All rights reserved.