Risk prediction models: II. External validation, model updating, and impact assessment

By Karel G. M. Moons, A. Kengne, Diederick E. Grobbee, Patrick Royston, Y. Vergouwe, Douglas G. Altman, M. Woodward. Heart, 2012

1,118 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Economics and Econometrics, General Health Professions

View Paper: https://www.semanticscholar.org/paper/33b55c5fc74e5612d0d6e840b06a3a1f1e6b201e

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Citations in the corpus

Transparent reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement. (2015, positive): “…in secondary care and assessed in similar participants but selected from primary care); or even in other types of participants (for example, model developed in adults and assessed in children, or developed for predicting fatal events and assessed for predicting nonfatal events) [13,15,17,21,22].”
Sample size considerations for the external validation of a multivariable prognostic model: a resampling study (2015, methodology): “External validation studies are a vital step in introducing a prognostic model, as they evaluate the performance and transportability of the model using data that were not involved in its development [2,48].”
Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement (2015, positive): “Prediction model studies can be broadly categorized as model development (12), model validation (with or without updating) (13) or a combination of both (Figure 3).”
Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement (2015, methodology): “In case of poor performance, the model can be updated or adjusted on the basis of the validation data set [13].”
A new framework to enhance the interpretation of external validation studies of clinical prediction models. (2015, positive): “These so-called (external) validation studies may range from temporal (eg, sample from the same hospital or primary care practice only later in time), to geographical (eg, sample from different hospital, region, or even country), to validations across different medical settings (eg, from secondary t”
External validation of prognostic models: what, why, how, when and where? (2020, background): “Some say that even with very slight updates the researchers are in fact developing a new prediction model, which in turn requires internal and external validation to assess validity [19, 28].”
Developing prediction models for clinical use using logistic regression: an overview. (2019, methodology): “Once a prediction model has been validated and is being implemented, the impact of the actual use of the model on the behavior and management of healthcare professionals and/or individual patients should be investigated through a model impact study (20).”
Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022, background): “The literature identi ﬁ es a randomized controlled trial (RCT) as the ideal comparative study design, randomizing patients individually or per cluster 5,9,49,68,86 .”
Effect of Ebola Progression in Liberia (2015, positive): “Prediction model studies can be broadly categorized as model development (12), model validation (with or without updating) (13) or a combination of both (Figure 3).”
Effect of Ebola Progression in Liberia (2015, positive): “Furthermore, TRIPOD is also not intended for impact studies that quantify the impact of using a prediction model on participant or doctors’ behavior and management, participant health outcomes, or cost-effectiveness of care, compared with not using the model.(13,38) Reporting guidelines for observat”