Prognosis Research Strategy (PROGRESS) 3: Prognostic Model Research

By Ewout W. Steyerberg, Karel G. M. Moons, D. A. van der Windt, J. Hayden, Pablo Perel, Sara Schroter, R. Riley, Harry Hemingway, Douglas G. Altman. PLoS Medicine, 2013

1,371 citations recorded by the source corpus.

In this article, the third in the PROGRESS series on prognostic factor research, Sara Schroter and colleagues review how prognostic models are developed and validated, and then address how prognostic models are assessed for their impact on practice and patient outcomes, illustrating these ideas with examples.

Subfields: Cardiology and Cardiovascular Medicine, Epidemiology, Surgery

View Paper: https://www.semanticscholar.org/paper/a9637d5114560469e66e55994ecf51c862f58d9a

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Citations in the corpus

Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints (2014, background): “The interest in the development, validation, and clinical application of clinical prediction models is increasing [1].”
Effect of Ebola Progression in Liberia (2015, positive): “recommended as a prerequisite for prediction model development, particularly if data are limited (6, 14, 15).”
Effect of Ebola Progression in Liberia (2015, neutral): “It is therefore not surprising, and fortunate, that very few prediction models, relative to the large number of models published, are widely implemented or used in clinical practice.(6) Prediction models in medicine have proliferated in recent years.”
Minimum sample size for developing a multivariable prediction model: PART II ‐ binary and time‐to‐event outcomes (2018, methodology): “First, let us re-express the right-hand side of Equation (3) in terms of sample size (n), number of candidate predictor parameters (p), and the Cox-Snell generalised R2.”
Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement (2015, neutral): “It is therefore not surprising, and fortunate, that very few prediction models, relative to the large number of models published, are widely implemented or used in clinical practice (Steyerberg et al, 2013).”
Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement (2015, positive): “Prediction models (also commonly called “prognostic models,” “risk scores,” or “prediction rules” [6]) are tools that combine multiple predictors by assigning relative weights to each predictor to obtain a risk or probability [1,2].”
Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies: The CHARMS Checklist (2014, methodology): “Regardless of the modelling technique, bootstrapping is particularly recommended in small datasets with many candidate predictors and when predictor selection techniques have been used [3,49,51, 55,58,59].”
Prognosis Research Strategy (PROGRESS) 2: Prognostic Factor Research (2013, methodology): “Some prognostic models for predicting individual outcome risk are being used in clinical practice, such as the GRACE score in acute myocardial infarction or the ADJUVANT! score in breast and other cancers, and we consider them further in the third paper in our series [17].”
Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement (2015, background): “Prediction models (also commonly called ‘‘prognostic models,’’ ‘‘risk scores,’’ or ‘‘prediction rules’’ [6]) are tools that combine multiple predictors by assigning relative weights to each predictor to obtain a risk or probability [1,2].”
Prediction Models of Functional Outcomes for Individuals in the Clinical High-Risk State for Psychosis or With Recent-Onset Depression: A Multimodal, Multisite Machine Learning Analysis (2018, neutral): “95 Munich 22 (33) 11 (22) 22 (34) 17 (31)”