cccrm (version 2.2.2)

icc: Intraclass correlation coefficient

Description

Estimates the one-way intraclass correlation coefficient using the variance components from a linear mixed model.

Usage

icc(dataset, ry, rind, covar = NULL, cl=0.95, control.lme=list())

Value

An object of class icc. Generic function summary show a summary of the results. The output is a list with the following components:

icc

Intraclass Correlation Coefficient estimate

model

nlme object with the fitted linear mixed model

vc

Variance components estimates

sigma

Variance components asymptotic covariance matrix

Arguments

dataset

an object of class data.frame.

ry

Character string. Name of the outcome in the data set.

rind

Character string. Name of the subject variable in the data set.

covar

Character vector. Name of covariables to include in the linear mixed model as fixed effects.

cl

Confidence level.

control.lme

A list of control values for the estimation algorithm used in lme function. For further details see lme help.

Author

Josep L. Carrasco

Details

The intraclass correlation coefficient (ICC) (Fleiss, 1986) is estimated using a linear mixed model with subjects (cluster) random effect and random error as components. The effects are assumed to follow Normal distributions with variances Sa and Se respectively. The expression of the ICC is \(Sa/(Sa + Se)\). This intraclass correlation coefficient is also known as reliability index. The variance components estimates are obtained from a linear mixed model (LMM) estimated by restricted maximum likelihood. The function lme from package nlme (Pinheiro et al., 2021) is used to estimate the LMM. The standard error of CCC is computed using an Taylor's series expansion of 1st order (Ver Hoef, 2012). Confidence interval is built by applying the Fisher's Z-transformation.

References

Fleiss, JL. (1986). Reliability of Measurement in *Design and analysis of clinical experiments*. New York:Wiley.

Pinheiro J, Bates D, DebRoy S, Sarkar D, R Core Team (2021). nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1-152, https://CRAN.R-project.org/package=nlme.

Ver Hoef, J.M. (2012) Who Invented the Delta Method?, The American Statistician, 66:2, 124-127.

Examples

data(bpres)
 
# Reliability of method 1 

newdat=bpres[(bpres$METODE==1),]
est=icc(newdat,"DIA","ID")
est
summary(est)

# Reliability of method 2 

newdat=bpres[(bpres$METODE==2),]
est=icc(newdat,"DIA","ID")
est
summary(est)