Impact of predictor measurement heterogeneity across settings on the performance of prediction models: A measurement error perspective.

ABSTRACT: It 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 scen

SUBMITTER: Luijken K 

PROVIDER: S-EPMC6619392 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature