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Validation of baseline and best performing linear multiple regression and linear mixed models was done using cross-validation.
The GLIMMIX Procedure for generalized linear mixed models was used for estimation.
It should be noted that the explained variance of both linear mixed models was low.
Proportion of variance for linear mixed models was calculated as described in Nakagawa and Schielzeth (2012).
18 The outcome in mixed models was QoL as a continuous variable.
Statistical power of the mixed models was calculated using post hoc Monte Carlo simulations.
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Generalized linear mixed models were used to assess predictors of acquisition.
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Linear mixed models are a powerful statistical tool for identifying genetic associations and avoiding confounding.
Generalised linear mixed models were used to analyse performance data.
Linear mixed models were applied in the analysis.
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