With balanced designs at first level (i.e., similar events for each subject, in similar numbers), this second-level analysis closely approximates a true mixed-effects design, with both within- and between-subject variance.
This method is as generally applicable as the method of maximum likelihood: to cases where designs are balanced or unbalanced, involving mixed effects and multiple traits or designs where random effects are correlated to each other.
For the analysis of these data, a linear mixed effects model for a blocked design with repeated measures was considered [ 34, 35].
However, there is a lack of research into performing Bayesian optimal design for nonlinear mixed effects models, especially those that require searches to be performed over several design variables.
Fully Bayesian experimental designs for nonlinear mixed effects models are presented, which involve the use of simulation-based optimal design methods to search over both continuous and discrete design spaces.
Several developments have facilitated the practical application and increased the general use of optimal design for nonlinear mixed effects models.
This is likely due to the fact that it is much more computationally intensive to perform optimal experimental design for nonlinear mixed effects models than it is to perform inference in the Bayesian framework.
We analyzed B-Pb in a repeated measures design using a mixed effects linear model.
To test for a systematic differentiation between color morphs in the trophic level δN while accounting for the correlation with δC [ 31] and accommodating the environmental variation across lakes, we designed a linear mixed effects model.
The great advantages of the mixed effects linear model for repeated measures designs is that if one of the outcome measures is missing (eg, if a participant fails to complete the personal stigma scale but does complete the professional stigma scale), the remaining data from the individual can still be retained.
