Internal validation of predictive models: efficiency of some procedures for logistic regression analysis.

By Ewout W. Steyerberg, F. Harrell, G. Borsboom, M. Eijkemans, Y. Vergouwe, J. Dik F. Habbema. Journal of Clinical Epidemiology, 2001

2,577 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/778188f49f6476ee2a100283bf1df859438d61b2

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Citations in the corpus

Dispersal traits determine plant response to habitat connectivity in an urban landscape (2011, background): “Species selection aimed at satisfying the statistical requirement of a minimum of ten occurrences which has been advised for logistic regression analysis (Steyerberg et al. 2001).”
Identifying Patients at High Risk for Neutropenic Complications During Chemotherapy for Metastatic Breast Cancer With Doxorubicin or Pegylated Liposomal Doxorubicin: The Development of a Prediction Model (2008, methodology): “Nonparametric bootstrapping was applied to test the internal validity of the final prediction model.(20,21) Resampled data (1000 iterations) were used to generate bootstrap estimates of the regression coefficients of the multivariable model.”
Comparative performance of generalized additive models and multivariate adaptive regression splines for statistical modelling of species distributions (2006, result): “Bootstrapping gives similar results to a cross-validation, but is less prone to bias (Steyerberg et al., 2001).”
Hyper-progressors after Immunotherapy: Analysis of Genomic Alterations Associated with Accelerated Growth Rate (2017, methodology): “predictive models, bootstrapping can be used when the sample size is too small to be split into a training and a validation set and/or there is no independent cross-validation cohort (17) (as is”
Developing clinical prediction models: a step-by-step guide (2024, positive): “47 Bootstrapping generally leads to more stable and less biased results, 93 and is therefore recommended for internal validation.”
External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges (2016, background): “16 26 Otherwise, poor performance could simply reflect small sample sizes, overfitting, and substandard development techniques.”
Assessing the performance of prediction models: a framework for some traditional and novel measures (2010, background): “Various epidemiologic and statistical issues need to be considered in a modeling strategy for empirical data.(1,19,20) When a model is developed, it is obvious that we want some quantification of its performance, such that we can judge whether the model is adequate for its purpose, or better than an”
Towards better clinical prediction models: seven steps for development and an ABCD for validation. (2014, methodology): “Using a random sample for model development, and the remaining patients for validation (‘split sample validation’) is a common, but suboptimal form of internal validation.(13) Better methods are cross-validation and bootstrap resampling, where samples are drawn with replacement from the development ”
Predicting survival after extracorporeal membrane oxygenation for severe acute respiratory failure. The Respiratory Extracorporeal Membrane Oxygenation Survival Prediction (RESP) score. (2014, neutral): “02 Bicarbonate infusion 424 (18) 197 (15) 227 (22) ,0.”