Calibrating predictive model estimates to support personalized medicine.

TitleCalibrating predictive model estimates to support personalized medicine.
Publication TypeJournal Article
Year of Publication2011
AuthorsJiang, X, Osl, M, Kim, J, Ohno-Machado, L
JournalJ Am Med Inform Assoc
Date Published2011 Oct 7
iDASH CategoryPatient Centered Research
AbstractObjectivePredictive models that generate individualized estimates for medically relevant outcomes are playing increasing roles in clinical care and translational research. However, current methods for calibrating these estimates lose valuable information. Our goal is to develop a new calibration method to conserve as much information as possible, and would compare favorably to existing methods in terms of important performance measures: discrimination and calibration.Material and methodsWe propose an adaptive technique that utilizes individualized confidence intervals (CIs) to calibrate predictions. We evaluate this new method, adaptive calibration of predictions (ACP), in artificial and real-world medical classification problems, in terms of areas under the ROC curves, the Hosmer-Lemeshow goodness-of-fit test, mean squared error, and computational complexity.ResultsACP compared favorably to other calibration methods such as binning, Platt scaling, and isotonic regression. In several experiments, binning, isotonic regression, and Platt scaling failed to improve the calibration of a logistic regression model, whereas ACP consistently improved the calibration while maintaining the same discrimination or even improving it in some experiments. In addition, the ACP algorithm is not computationally expensive.LimitationsThe calculation of CIs for individual predictions may be cumbersome for certain predictive models. ACP is not completely parameter-free: the length of the CI employed may affect its results.ConclusionsACP can generate estimates that may be more suitable for individualized predictions than estimates that are calibrated using existing methods. Further studies are necessary to explore the limitations of ACP.
Alternate JournalJ Am Med Inform Assoc
PubMed ID21984587