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Comparing methods addressing multi-collinearity when developing prediction models

Authors :
Leeuwenberg, Artuur M.
van Smeden, Maarten
Langendijk, Johannes A.
van der Schaaf, Arjen
Mauer, Murielle E.
Moons, Karel G. M.
Reitsma, Johannes B.
Schuit, Ewoud
Publication Year :
2021

Abstract

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).}

Subjects

Subjects :
Statistics - Methodology
60
G.3

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2101.01603
Document Type :
Working Paper