gms | German Medical Science

Background: Boosting techniques from the field of statistical learning have grown to be a popular tool for estimating and selecting predictor effects in various regression models and can roughly be separated into two general approaches, namely gradient boosting and likelihood-based boosting. An extensive framework has been proposed in order to fit generalised mixed models based on boosting. However, for the case of cluster-constant covariates, likelihood-based boosting approaches tend to mischoose variables in the selection step leading to wrong estimates.

Methods: We propose an improved boosting algorithm for linear mixed models where the random effects are properly weighted, disentangled from the fixed effects updating scheme and corrected for correlations with cluster-constant covariates in order to improve quality of estimates and in addition reduce the computational effort.

Results: The method outperforms current state-of-the-art approaches from boosting and maximum likelihood inference which is shown via simulations and various data examples.

Conclusion: In conclusion, the new algorithm solves the problem of wrongly estimated coefficients and due to the reduced computational burden makes the algorithm more applicable to real world scenarios.

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.