Updating methods improved the performance of a clinical prediction model in new patients.

By K. Janssen, Karel G. M. Moons, C. Kalkman, Diederick E. Grobbee, Y. Vergouwe. Journal of Clinical Epidemiology, 2008

389 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Epidemiology, Health Informatics

View Paper: https://www.semanticscholar.org/paper/cdae88203c4c2dafd83da21d95a23c906aefface

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Citations in the corpus

Prognosis and prognostic research: application and impact of prognostic models in clinical practice (2009, methodology): “When the model was validated in Dutch primary care patients, the negative predictive value was only 88% (85% to 91%) and sensitivity 79% (74% to 84%).”
A new framework to enhance the interpretation of external validation studies of clinical prediction models. (2015, positive): “Specifically, a poor calibrationin-the-large may be overcome by re-estimating its intercept or baseline hazard (if applicable) in the validation sample (intercept update) [3,11,34,36,37].”
External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges (2016, background): “51 64 65 In particular, missing predictor values are likely in some participants and there may be systematically missing predictors, which occurs when a predictor is not measured for any individuals in one or more studies (clusters).”
External validation of a Cox prognostic model: principles and methods (2013, methodology): “What can be done? Whereas methodology exists and has been applied to logistic regression models [41,42], the methods do not transfer to Cox models.”
Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies: The CHARMS Checklist (2014, background): “When a review includes both development and validation studies of the same model(s), or several external validations of the same model, reporting differences in frequency (binary) and distribution (continuous) of the predictors and outcomes across the study samples is recommended, as a different cas”
Prognosis Research Strategy (PROGRESS) 3: Prognostic Model Research (2013, background): “Ideally there should be an ongoing process of model validation and updating [5,9,46–48].”
Risk prediction models: II. External validation, model updating, and impact assessment (2012, methodology): “An alternative solution to redevelopment, is to adjust or update existing prediction models with the external validation set data at hand.(11) 12 In the first article of this series of two,(1) we have presented the focus of this series and an overview of the consecutive steps involved in prediction ”