  Clinical prediction models are developed widely across medical disciplines.
When predictors in such models are highly collinear, unexpected or spurious
predictor-outcome associations may occur, thereby potentially reducing
face-validity and explainability of the prediction model. Collinearity can be
dealt with by exclusion of collinear predictors, but when there is no a priori
motivation (besides collinearity) to include or exclude specific predictors,
such an approach is arbitrary and possibly inappropriate. We compare different
methods to address collinearity, including shrinkage, dimensionality reduction,
and constrained optimization. The effectiveness of these methods is illustrated
via simulations. In the conducted simulations, no effect of collinearity was
observed on predictive outcomes. However, a negative effect of collinearity on
the stability of predictor selection was found, affecting all compared methods,
but in particular methods that perform strong predictor selection (e.g.,
Lasso).}
