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United States , Nina Nouhravesh , American Heart Association , American College Of Cardiology Annual Scientific Session , Duke Clinical Research Institute , Harvard Medical School , American College , Annual Scientific Session , Harvard Study , American Journal , Clinical Nutrition , Dietary Guidelines , Duke Clinical Research , United States Today ,

Evaluation of clinical prediction models (part 2): how to undertake an external validation study

External validation studies are an important but often neglected part of prediction model research. In this article, the second in a series on model evaluation, Riley and colleagues explain what an external validation study entails and describe the key steps involved, from establishing a high quality dataset to evaluating a model’s predictive performance and clinical usefulness.

A clinical prediction model is used to calculate predictions for an individual conditional on their characteristics. Such predictions might be of a continuous value (eg, blood pressure, fat mass) or the probability of a particular event occurring (eg, disease recurrence), and are often in the context of a particular time point (eg, probability of disease recurrence within the next 12 months). Clinical prediction models are traditionally based on a regression equation but are increasingly derived using artificial intelligence or machine learning methods (eg, random forests, neural networks). Regardless ....

United Kingdom , Lucinda Archer , Lauraj Bonnett , Glenp Martin , Paula Dhiman , Richardd Riley , Kym Ie Snell , Garys Collins , University Of Birmingham , Duke Clinical Research Institute , Cancer Research United Kingdom , Better Methods Research , Health Improvement Network , Research Council , Sciences Research Council , United Kingdom Department Of Health , Birmingham Biomedical Research Centre , University Hospitals Birmingham , National Institute For Health , Clinical Practice Research Datalink , Care Research , Transparent Reporting , Individual Prognosis Or Diagnosis , United Kingdom Biobank , Risk Of Bias , Participant Selection ,