A framework for evaluating and distinguishing validity and generalization of prediction models
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Introduction It is widely acknowledged that newly developed diagnostic or prognostic prediction models should be validated in samples with different (i.e. not included in the sample from which the model was developed) but related (i.e. similar characteristics or case mix) individuals . However, criteria for ’different but related’ are lacking, compromising structured model validation studies. Based on previous recommendations we describe a framework of methodological steps for analyzing and interpreting the results of prediction model validation studies, to enhance inferences about the model’s generalizability across populations, clinical practices and settings.
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