• K Luijken, GroenwoldR H HRHH0000-0001-9238-6999Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands., B Van Calster, E W Steyerberg, and M van Smeden.
• Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.
• Stat Med. 2019 Aug 15; 38 (18): 3444-3459.

AbstractIt is widely acknowledged that the predictive performance of clinical prediction models should be studied in patients that were not part of the data in which the model was derived. Out-of-sample performance can be hampered when predictors are measured differently at derivation and external validation. This may occur, for instance, when predictors are measured using different measurement protocols or when tests are produced by different manufacturers. Although such heterogeneity in predictor measurement between derivation and validation data is common, the impact on the out-of-sample performance is not well studied. Using analytical and simulation approaches, we examined out-of-sample performance of prediction models under various scenarios of heterogeneous predictor measurement. These scenarios were defined and clarified using an established taxonomy of measurement error models. The results of our simulations indicate that predictor measurement heterogeneity can induce miscalibration of prediction and affects discrimination and overall predictive accuracy, to extents that the prediction model may no longer be considered clinically useful. The measurement error taxonomy was found to be helpful in identifying and predicting effects of heterogeneous predictor measurements between settings of prediction model derivation and validation. Our work indicates that homogeneity of measurement strategies across settings is of paramount importance in prediction research.© 2019 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

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