Do you include the baseline in a mixed model?
Personally, I would by default include the baseline in any model in the type of trial you are describing (and for the type of mixed model you describe always include the time by baseline interaction). The most usual way is to indeed include the baseline as a fixed covariate. Using it as a random effect is of course also possible, but less common.
Can a baseline be used as a random effect?
Using it as a random effect is of course also possible, but less common. The final option is to use the baseline as yet another observation instead of as a model term. This assumes joint (multivariate-)normality (assuming you are using a normal model) of the error terms across the visits including the baseline.
Is it important to have time by baseline interaction?
However, if one does include the baseline, it is usually recommended to have a time (as a factor) by baseline interaction, because the importance of the baseline will usually decrease over time.
What are the parameters of a linear mixed model?
Now the data are random variables, and the parameters are random variables (at one level), but fixed at the highest level (for example, we still assume some overall population mean, μ ).
What are the results of a mixed effect model?
In these results, the estimated standard deviation (S) of the random error term is 0.17. The model explains 92.33% of the variation in the yield of alfalfa plants. After adjusting for the number of fixed factor parameters in the model, the percentage reduces to 90.2%.
What do you need to know about mixed effect modeling?
Below are some important terms to know for understanding the statistical concepts used in mixed models: Crossed designs refer to the within-subject variables (i.e. timepoint, condition, etc.). Crossed designs occur when multiple measurements are associated with multiple grouping variables.