ccclonw {cccrm}

Weighted Concordance Correlation Coefficient for longitudinal repeated measures estimated by variance components

Description

Estimation of the concordance correlation coefficient (CCC) for repeated measurements using the variance components from a linear mixed model. The appropriate intraclass correlation coefficient is used as estimator of the concordance correlation coefficient. Weights are assigned to repeated measurements in the CCC computation process.

Usage

ccclonw(dataset, ry, rind, rtime, rmet, vecD, covar = NULL, rho = 0, 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.

rtime

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

rmet

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

vecD

Vector of weigths. The length of the vector must be the same as the number of repeated measures.

covar

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

rho

Within subject correlation structure. A value of 0 (default option) stands for compound simmetry and 1 is used for autoregressive of order 1 structure.

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 which expression is modified accordingly to assign different weights to each repeated measurement (see Carrasco et al, 2009; Carrasco et al, 2013). 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; 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.

See Also

ccclon

Examples

data(bfat)
estccc<-ccclonw(bfat,"BF","SUBJECT","VISITNO","MET",vecD=c(2,1,1))
estccc
summary(estccc)