How to develop a more accurate risk prediction model when there are few events

By M. Pavlou, G. Ambler, S. Seaman, O. Guttmann, P. Elliott, M. King, R. Omar. British medical journal, 2015

644 citations recorded by the source corpus.

When the number of events is low relative to the number of predictors, standard regression could produce overﬁtted risk models that make inaccurate predictions. Use of penalised regression may improve the accuracy of risk prediction

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/bfbe594aaa7720d8dc8d90427bf55874ad3349e9

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Citations in the corpus

Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges (2016, background): “This particularly becomes a problem when developing risk models for rare events.(22,23) For instance, in our data example, we had 43 predictor variables and only 101 events.”
Minimum sample size for developing a multivariable prediction model: PART II ‐ binary and time‐to‐event outcomes (2018, methodology): “18 SVH from equation (26) is larger than that chosen for criterion (i), as then the sample size required to meet criteria (ii) will be larger than that for criteria (i).”
Developing clinical prediction models: a step-by-step guide (2024, positive): “69-71 Penalisation methods such as ridge, LASSO (least absolute shrinkage and selection operator), and elastic net generally lead to smaller absolute values of the coefficients—that is, they shrink coefficients towards zero—compared with maximum likelihood estimation.”
Sample size for binary logistic prediction models: Beyond events per variable criteria (2018, background): “The observed tendency to underfitting of Ridge regression is consistent with other recent simulation studies.(16,48) In smaller samples, backwards elimination with conventional p1⁄4 0.”