cccvc {cccrm}

Concordance Correlation Coefficient estimated by Variance Components

Description

Estimation of the concordance correlation coefficient for non-repeated measurements and non-longitudinal repeated measurements (replicates) using the variance components from a linear mixed model. The appropriate intraclass correlation coefficient is used as estimator of the concordance correlation coefficient.

Usage

cccvc(dataset, ry, rind, rmet, covar = NULL, int = FALSE, cl=0.95, control.lme=list())

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.

rmet

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

covar

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

int

Boolean indicating if the subject-method interaction has to be included in the model

cl

Confidence level.

control.lme

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

Details

The concordance correlation coefficient is estimated using the appropriate intraclass correlation coefficient (see Carrasco and Jover, 2003; Carrasco et al., 2009; Carrasco et al, 2013). The scenarios considered are non-repeated measurements (only one measurement by subject and method) and non-longitudinal repeated measurements, i.e. replicates (multiple measurements by subject and method). 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.

Value

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

ccc

Concordance Correlation Coefficient estimate

model

nlme object with the fitted linear mixed model

vc

Variance components estimates

sigma

Variance components asymptotic covariance matrix

Author(s)

Josep Puig-Martinez and Josep L. Carrasco

References

Carrasco, JL; Jover, L. (2003). Estimating the generalized concordance correlation coefficient through variance components. Biometrics, 59, 849:858.

Carrasco, JL; King, TS; Chinchilli, VM. (2009). The concordance correlation coefficient for repeated measures estimated by variance components. Journal of Biopharmaceutical Statistics, 19, 90:105.

Carrasco, JL; Phillips, BR; Puig-Martinez, J; King, TS; Chinchilli, VM. (2013). Estimation of the concordance correlation coefficient for repeated measures using SAS and R. Computer Methods and Programs in Biomedicine, 109, 293-304.

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)

# Scenario 1. 
#Only 1 measure by subject and method. 
#No subject-method interaction included in the model.

newdat=bpres[bpres$NM==1,]
estccc=cccvc(newdat,"DIA","ID","METODE")
estccc
summary(estccc)

# Scenario 2. 
#Two measures by subject and method. 
#No subject-method interaction included in the model.

estccc=cccvc(bpres,"DIA","ID","METODE")
estccc
summary(estccc)