Substantial effective sample sizes were required for external validation studies of predictive logistic regression models.

By Y. Vergouwe, Ewout W. Steyerberg, Marinus J. C. Eijkemans, J. Dik F. Habbema. Journal of Clinical Epidemiology, 2005

616 citations recorded by the source corpus.

Subfields: Pulmonary and Respiratory Medicine, Statistics and Probability, Surgery

View Paper: https://www.semanticscholar.org/paper/d8c3b0e83e3c08456ead6c80f0e8626065256868

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Citations in the corpus

Predicting Outcome after Traumatic Brain Injury: Development and International Validation of Prognostic Scores Based on Admission Characteristics (2008, neutral): “500) [26].”
External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges (2016, background): “Further, sample size was often small, with 46% having fewer than 100 events, which is a minimum effective sample size suggested for external validation 40 41 (although an increase to 200 was recently proposed to assess calibration 37 41 ).”
Prognosis Research Strategy (PROGRESS) 3: Prognostic Model Research (2013, background): “Validation studies provide estimates of a model’s ability to discriminate between patients with different outcomes and of the agreement between predicted and observed risks [35].”
Prognosis and prognostic research: validating a prognostic model (2009, background): “—Neither internal nor temporal validation examines the generalisability of the model, for which it is necessary to use new data collected from an appropriate (similar) patient population in a different centre. The data can be retrospective data and so external validation is possible for prediction m”
Sample size considerations for the external validation of a multivariable prognostic model: a resampling study (2015, methodology): “to detect pre-specified changes in the c-statistic) and recommends that models developed using logistic regression are evaluated with a minimum of 100 events [12].”
Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies: The CHARMS Checklist (2014, background): “For studies validating prediction models, sample size considerations are not well established, but a minimum of 100 events and 100 non-events have been suggested [102].”
Sample size planning for statistical power and accuracy in parameter estimation. (2008, neutral): “Vergouwe et al. (2005) illustrate sample size planning for externally validating various logistic regression models using data from patients with metastatic testicular cancer.”