Quick question on GLMM with repeated measures (crossover) design and how to deal with missing covariate data.
In this context, 28 participants complete 2 conditions (crossover design with 1 week washout between conditions) comparing glucose effect (AUC) of 1) sugary drink vs. 2) water. I am adjusting for several covariates in the model (e.g. BMI, age, fasting values, physical activity before each trial condition).
For the physical activity covariate - I have missing data for both conditions in 6 participants and partial missing data in a further 4 participants (i.e. I have complete covariate data for only 1 condition).
I see when I control for prior physical activity as a covariate - my total observations are 22 (suggesting that the participants with fully missing PA data are being dropped).
QU1: Is there a way to 'deal with this' or keep these participants in somehow to avoid this loss of sample size (imputation?) - or does one just have to concede this as a limitation?
QU2: What is the mixed model actually doing with the participants with partially missing PA data (i.e. only missing for one of the conditions) - as I see these seem to be kept in the model?