Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker

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

850 citations recorded by the source corpus.

Subfields: Economics and Econometrics, General Health Professions, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/b48a4ac75d39012451c7d088ca95632296e986a9

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Citations in the corpus

Prediction models for cardiovascular disease risk in the general population: systematic review (2016, neutral): “We differentiated between articles reporting on the development 17-19 or external validation 19-21 of one or more prediction models (box 1).”
Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement (2015, neutral): “recommended as a prerequisite for prediction model development, particularly if data are limited (6, 14, 15).”
Transparent reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement. (2015, neutral): “The TRIPOD Statement covers studies that report solely development [12,15], both development and external validation, and solely external validation (with or without updating), of a prediction model [14] (Fig.”
Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement (2015, methodology): “recommended as a prerequisite for prediction model development, particularly if data are limited (6, 14, 15).”
Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022, methodology): “This scoping review provides an easy-to-use overview and summary of the currently available actionable guidelines and quality criteria driven by the six phases of the AIPM development, evaluation, and implementation cycle: (1) data preparation, (2) AIPM development, (3) AIPM validation, (4) software”
External validation of multivariable prediction models: a systematic review of methodological conduct and reporting (2014, background): “Key characteristics, such as calibration and discrimination, are widely recommended aspects to evaluate [9,12-15,26,45,46].”
Sample size for binary logistic prediction models: Beyond events per variable criteria (2018, background): “However, some authors have expressed concerns that that the EPV 10 rule-of-thumb is not based on convincing scientific reasoning.(17) The rule also did not perform well in large-scale simulation studies.”
A new framework to enhance the interpretation of external validation studies of clinical prediction models. (2015, neutral): “By deﬁnition, the calibration-in-the-large is always optimal (0) in the development sample of the prediction model [11,31].”
Review and evaluation of penalised regression methods for risk prediction in low‐dimensional data with few events (2015, critical): “This cut-off has been challenged as not being based on convincing scientific reasoning [3], and in fact, it is based on empirical evidence for obtaining valid estimates of standard errors for regression coefficients, rather than reliable risk prediction [9, 10].”
Effect of Ebola Progression in Liberia (2015, neutral): “External validation may use participant data collected by the same investigators, typically using the same predictor and outcome definitions and measurements, but sampled from a later period (temporal or narrow validation); by other investigators in another hospital or country, sometimes using diffe”