Companion R package for the course "Statistical analysis of correlated and repeated measurements for health science researchers" taught by the section of Biostatistics of the University of Copenhagen. It implements linear mixed models where the model for the variance-covariance of the residuals is specified via patterns (compound symmetry, unstructured). Statistical inference for mean, variance, and correlation parameters is performed based on the observed information and a Satterthwaite degrees of freedom. Normalized residuals are provided to assess model misspecification. Statistical inference can be performed for arbitrary linear combination(s) of model coefficients. Predictions can be computed conditional to covariates only or also to outcome values.
Currently only four types of model for the residual variance-covariance matrix are available:
"ID"
: Identity (no correlation, constant variance)
"IND"
: Independent (no correlation, time-specific variance)
"CS"
: compound symmetry (constant correlation, constant variable)
"UN"
: unstructured (time-specific correlation, time-specific variable)
It possible to stratify the last two structure with respect to a categorical variable.
The package is based on the nlme::gls
function and the PROC MIXED from the SAS software.
Adjustment for multiple comparisons is based on the multcomp package.
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