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General linear mixed models using the MIXED procedure in SAS SAS Institutee, Inc., Cary, NC, USA) were used to examine the factors associated with decline in cognitive function over a 6-year period as a function of fall status.

All models were fitted as nonlinear mixed models using the SAS macro NLINMIX.

Data were analyzed in linear mixed models using repeated measures within herd where MFP was considered the outcome variable.

This work provides an assessment of layerwise mixed models using least-squares formulation for the coupled electromechanical static analysis of multilayered plates.

The relationship between TBDs seroprevalence and the risk factors was assessed by multivariable analysis using standard logistic regression models and mixed models using the farm as a random effect.

We performed linear mixed models using S4 classes version 0.999375-28 in R. The data we used are included in the Appendix S1.

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Linear mixed models use all available longitudinal data while adjusting for within-patient correlation.

In most linear mixed models used in animal breeding, it is usually assumed that residual variance is constant across genotypes.

Linear mixed models use all data available at each time point; thus missing data imputation was not undertaken.

The mixed models used in this study are complex models that are not easily interpreted – especially if many degrees of smoothing have been used.

Animal models are generalized linear mixed models used in evolutionary biology and animal breeding to identify the genetic part of traits.

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