LASSO regression

Regression models are commonly used in statistical analyses1,2. A popular use is to model the predicted risk of a likely outcome. Unfortunately, applying standard regression methods to a set of candidate variables to generate a model tends to lead to overfitting in terms of the number of variables ultimately included in the model, and also overestimation of how well the model performs in terms of using the included variables to explain the observed variability (‘optimism bias’). The model tends to perform particularly poorly with predicting observations more ‘extreme’ (very high or very low) risk. Various (penalized or regularization) regression techniques, can be used to address these problems. LASSO (Least Absolute Shrinkage and Selection Operator) regression, a shrinkage and variable selection method for regression models, is an attractive option as it addresses both problems3. Gains in computational power and incorporation into statistical software also mean that its computer-intensive nature is no longer off-putting. One area it has been used is for handing genetic data as the number of potential predictors is often large relative to the number of observations, and there is often little or no a-priori knowledge to inform variable selection.