For example, we can check whether a specific random effect at a certain grouping level is warranted or whether it can be eliminated.
However, in this model we assume that there is no intra-cluster correlation anymore after having taken into account prognostic factors and after adjusting for a subject specific random effect term.
A random intercept (a community- specific random effect) accounted for the clustering of observations at the community level.
For the specific random effects ϵij, we assumed an exchangeable distribution, whereas for the common random effect φi, we assessed two different specifications: an exchangeable distribution, and a conditional autoregressive distribution (CAR).
A normal density for the individual specific random effect is assumed.
A common modeling approach is then to include an individual specific random effect ind i for each individual.
For longitudinal data, subject specific random effect models account for the correlation among measurements on the same subject through the concept of heterogeneity among subjects; some subjects are intrinsically high responders, others low-responders.
Subjects contributed up to four follow-up measurements (days 3,7,14, and 21) for analysis, therefore a subject-specific random intercept was used to account for the expected positive correlation between marker levels from the same individual.
The mixed-effects modeling approach adopted in the current work estimated a variance-covariance matrix for participant-specific random effects, including the cue-utilization coefficients for the four stimulus dimensions.
