WS.Corr.Mixed.SAS {CorrMixed} R Documentation

## Estimate within-subject (test-retest) correlations based on a mixed-effects model using the SAS proc MIXED output.

### Description

This function allows for the estimation of the within-subject correlations using a general and flexible modeling approach that allows at the same time to capture hierarchies in the data, the presence of covariates, and the derivation of correlation estimates. The output of proc MIXED (SAS) is used as the input for this function. Confidence intervals for the correlations based on the Delta method are provided.

### Usage

WS.Corr.Mixed.SAS(Model, D, Sigma2, Asycov, Rho, Tau2, Alpha=0.05, Time)


### Arguments

 Model The type of model that should be fitted. Model=1: random intercept model, Model=2: random intercept and serial correlation, and Model=3: random intercept, slope, and serial correlation. Default Model=1. D The D matrix of the fitted model. Sigma2 The residual variance. Asycov The asymptotic correlation matrix of covariance parameter estimates. Rho The ρ component of the fitted model which determines the matrix H_{i}, ρ(|t_{ij}-t_{ik}|). This component is only needed when serial correlation is involved, i.e., when Model 2 or 3 used. Tau2 The τ^2 component of the fitted model. This component is only needed when serial correlation is involved (i.e., when Model 2 or 3 used), \varepsilon_{2} \sim N(0, τ^2 H_{i})). Alpha The α-level to be used in the computation of the Confidence Intervals around the within-subject correlation. The Confidence Intervals are based on the Delta method. Default Alpha=0.05. Time The time points available in the dataset on which the analysis was conducted.

### Value

 Model The type of model that was fitted. R The estimated within-subject correlations. Alpha The α-level used to computed the Confidence Intervals around R. CI.Upper The upper bounds of the confidence intervals (Delta-method based). CI.Lower The lower bounds of the confidence intervals (Delta-method based). Time The time values in the dataset.

### Author(s)

Wim Van der Elst, Geert Molenberghs, Ralf-Dieter Hilgers, & Nicole Heussen

### References

Van der Elst, W., Molenberghs, G., Hilgers, R., & Heussen, N. (2015). Estimating the reliability of repeatedly measured endpoints based on linear mixed-effects models. A tutorial. Submitted.

WS.Corr.Mixed

### Examples

# Open data
data(Example.Data)

# Estimate R and Delta method-based CI
# based on SAS output of fitted Model 2

# First specify asycov matrix
Asy_mat <- matrix(c(129170, -10248, -12.0814, -74.8605,
-10248, 25894, 21.0976, -50.1059,
-12.0814, 21.0976, 0.07791, 1.2120,
-74.8605, -50.1059, 1.212, 370.65), nrow = 4)
Model2_SAS <-  WS.Corr.Mixed.SAS(Model="Model 2",
D=500.98, Tau2=892.97, Rho=3.6302, Sigma2=190.09,
Asycov = Asy_mat, Time=c(1:45))
summary(Model2_SAS)
plot(Model2_SAS)


[Package CorrMixed version 1.0 Index]