reduction in the number of parameters to estimate.We choose to model the random subject-specific effect instead of including dummy subject covariates in our model because: Random effects representing individual variation or auto correlation/spatial effects that imply dependent (correlated) errors Linear Mixed Model (LMM), also known as Mixed Linear Model has 2 components:įixed effect (e.g, gender, age, diet, time) Hence, we like to account for these correlations. Spatial data: (e.g., measurement of individuals living in the same neighborhood are correlated).Longitudinal data: (e.g., individual’s cholesterol tracked over time are correlated): “data collected repeatedly over time in an observational study are termed longitudinal.” ( Schabenberger and Pierce 2001).Repeated measurements: (e.g., measurement of cholesterol over time can be correlated) “If data are collected repeatedly on experimental material to which treatments were applied initially, the data is a repeated measure.” ( Schabenberger and Pierce 2001).Clustered measurements: (e.g., blood pressure measurements of people in the same family can be correlated).Multivariate measurements on different individuals: (e.g., a person’s blood pressure, fat, etc are correlated).37 Sensitivity Analysis/ Robustness Check.9 Nonlinear and Generalized Linear Mixed Models.
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