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GLMM is the extension of GLM by introducing random effects into the linear predictor of the GLM [ 16, 17].
For longitudinal data with repeated observations, the correlation structure may be taken into account by introducing random effects in the proposed zero-inflated model.
The correlation among responses pertaining to one subject is now induced by introducing random effects, which can be regarded as subject-specific terms [ 19, 20].
Using 'Proc mixed' in SAS, we embedded the analysis in a multilevel design by introducing random effects to adjust for the multiple observations within specialties, clinics, trainers and students and to capture within-cluster associations.
Each subject may differ to some degree from the group as a whole, and this is dealt with by introducing random effects which were included for the intercept (i.e. how much the intercept from a subject's individual regression line deviated from the overall intercept), and for the percent of MVC) to allow for differences in how an individual responded to increasing force generation.
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When the assumption of a fixed effect is acceptable, introducing random effects might unnecessarily widen confidence intervals and reduce power.
Therefore, it is significant and of prime importance to introduce random effects in the models.
We will do so by including patient-supplied variables at the patient level in the analytic model and then introducing random-effects terms for the MCGs to account for within-MCG correlation.
We further extended this model by introducing random and systematic variation in enzyme activity.
The study was later extended, e.g., by introducing random mutation of interaction strength in various replicator networks [ 7] or by introducing random growth in replicator networks [ 8, 9].
A mutated sublibrary was created by introducing random mutations in the KM10 scFv gene.
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