Abstract
The performance of a prediction or measurement method is often evaluated by computing the correlation coefficient and/or the regression of predictions on true (reference) values. These provide, however, only a poor description of predictive performance. The mean squared prediction error (precision) and the mean prediction error (bias) provide better descriptions of predictive performance. These quantities are easily computed, and can be used to compare prediction methods to absolute standards or to one another. The measures, however, are unreliable when the reference method is imprecise. The use of these measures is discussed and illustrated.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
W. J. Dixon and F. J. Massey.Introduction to Statistical Analysis, 3rd ed., McGraw-Hill, New York, 1969, p. 98.
R. G. Miller. The jacknife—A review.Biometrika 61:1–15 (1974).
W. J. Dixon and F. J. Massey.Introduction to Statistical Analysis, 3rd ed., McGraw-Hill, New York, 1969, p. 349.
W. J. Dixon and F. J. Massey.Introduction to Statistical Analysis, 3rd ed., McGraw-Hill, New York, 1969, pp. 341–342.
Author information
Authors and Affiliations
Additional information
Work supported in part by Grants GM 26676 and GM 26691
An erratum to this article is available at http://dx.doi.org/10.1007/BF01062337.
Rights and permissions
About this article
Cite this article
Sheiner, L.B., Beal, S.L. Some suggestions for measuring predictive performance. Journal of Pharmacokinetics and Biopharmaceutics 9, 503–512 (1981). https://doi.org/10.1007/BF01060893
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/BF01060893