Clinical Prediction Models—a Practical Approach to Development, Validation and Updating

By G. Bedogni. 2009

1,267 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine, Statistics and Probability, Surgery

View Paper: https://www.semanticscholar.org/paper/d7542ce43ca5884e35d3fdb847ed4f46d419c8d3

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In Cardiology and Cardiovascular Medicine

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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, 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].”
An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies (2011, background): “Regression adjustment results in increased precision for continuous outcomes and increased statistical power for continuous, binary, and time-to-event outcomes (Steyerberg, 2009).”
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].”
Using methods from the data mining and machine learning literature for disease classification and prediction: A case study examining classification of heart failure sub-types (2013, neutral): “First, we calculated the area under the receiver operating characteristic curve (AUC), which is equivalent to the c-statistic [28,30].”
Applied Predictive Modeling (2013, methodology): “Over-fitting has been discussed in the fields of forecasting (Clark 2004), medical research (Simon et al. 2003; Steyerberg 2010), chemometrics (Gowen et al.”
Transparent reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement. (2015, neutral): “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].”
Assessing Calibration of Prognostic Risk Scores (2013, methodology): “Limitations of the Hosmer–Lemeshow test are well known and include its dependence on arbitrary groupings of patients, poor power in small data sets as well as the fact that it only results in a p value.(1,9) A calibration plot is sometimes described as a visual representation of the Hosmer–Lemeshow ”
Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement (2015, neutral): “In the prognostic setting, predictions can be used for planning lifestyle or therapeutic decisions based on the risk for developing a particular outcome or state of health within a specific period (1, 2).”
Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards (2016, methodology): “Model calibration, which is the agreement between a model’s predicted probability and the actual probability of an event, was measured in several ways using the discrete-time framework in the validation dataset (15).”
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’ (Steyerberg et al, 2013) are tools that combine multiple predictors by assigning relative weights to each predictor to obtain a risk or probability (Steyerberg, 2009; Moons et al, 2009b).”
Identifying critically ill patients who benefit the most from nutrition therapy: the development and initial validation of a novel risk assessment tool (2011, methodology): “70) and the generalized max-rescaled R-squared statistic [18,22].”
Prognosis Research Strategy (PROGRESS) 3: Prognostic Model Research (2013, methodology): “Methods for developing models have been widely discussed [3,5,10] and will not be described in detail here.”
Development and validation of the Emergency Department Assessment of Chest pain Score and 2 h accelerated diagnostic protocol (2014, methodology): “The statistical prediction model was then converted into a simpler, more user-friendly scoring system that could be easily calculated by clinicians without electronic assistance.(18) The coefficients from the statistical prediction model were converted into integers by multiplying with whole numbers”