ccclonw: Weighted Concordance Correlation Coefficient for longitudinal...

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/ccclonw.R

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

1
ccclonw(dataset, ry, rind, rtime, rmet, vecD, covar = NULL, rho = 0, cl = 0.95)

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.

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 estimated by restricted maximum likelihood. The standard error of CCC is computed using an Taylor's series expansion of 1st order (delta method). 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

Summary of the 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.

See Also

ccclon

Examples

1
2
3
4
data(bfat)
estccc<-ccclonw(bfat,"BF","SUBJECT","VISITNO","MET",vecD=c(2,1,1))
estccc
summary(estccc)

Example output

Loading required package: nlme
Loading required package: gdata
sh: 1: cannot create /dev/null: Permission denied
gdata: Unable to locate valid perl interpreter
gdata: 
gdata: read.xls() will be unable to read Excel XLS and XLSX files
gdata: unless the 'perl=' argument is used to specify the location of a
gdata: valid perl intrpreter.
gdata: 
gdata: (To avoid display of this message in the future, please ensure
gdata: perl is installed and available on the executable search path.)
sh: 1: cannot create /dev/null: Permission denied
gdata: Unable to load perl libaries needed by read.xls()
gdata: to support 'XLX' (Excel 97-2004) files.

gdata: Unable to load perl libaries needed by read.xls()
gdata: to support 'XLSX' (Excel 2007+) files.

gdata: Run the function 'installXLSXsupport()'
gdata: to automatically download and install the perl
gdata: libaries needed to support Excel XLS and XLSX formats.

Attaching package: 'gdata'

The following object is masked from 'package:stats':

    nobs

The following object is masked from 'package:utils':

    object.size

The following object is masked from 'package:base':

    startsWith

Warning message:
In c(-1, 1) * qnorm(1 - alpha/2) * se.z :
  Recycling array of length 1 in vector-array arithmetic is deprecated.
  Use c() or as.vector() instead.

CCC estimated by variance components: 
       CCC  LL CI 95%  UL CI 95%     SE CCC 
0.56588399 0.45925337 0.65643840 0.05031897 

Linear mixed-effects model fit by REML
 Data: dades 
       AIC      BIC    logLik
  2029.712 2071.574 -1004.856

Random effects:
 Composite Structure: Blocked

 Block 1: (Intercept)
 Formula: ~1 | ind
        (Intercept)
StdDev:    2.931214

 Block 2: met1, met2
 Formula: ~-1 + met | ind
 Structure: Multiple of an Identity
            met1     met2
StdDev: 1.451976 1.451976

 Block 3: time2, time3, time4
 Formula: ~-1 + time | ind
 Structure: Multiple of an Identity
            time2     time3     time4  Residual
StdDev: 0.9593789 0.9593789 0.9593789 0.8773658

Fixed effects: list(form) 
                Value Std.Error  DF  t-value p-value
(Intercept) 23.655569 0.3887194 405 60.85513       0
met2        -2.116536 0.2649438 405 -7.98862       0
time3        1.591701 0.2030366 405  7.83948       0
time4        1.462849 0.2030366 405  7.20485       0
met2:time3  -1.635889 0.1937777 405 -8.44209       0
met2:time4  -1.432546 0.1937777 405 -7.39273       0
 Correlation: 
           (Intr) met2   time3  time4  mt2:t3
met2       -0.341                            
time3      -0.261  0.175                     
time4      -0.261  0.175  0.500              
met2:time3  0.125 -0.366 -0.477 -0.239       
met2:time4  0.125 -0.366 -0.239 -0.477  0.500

Standardized Within-Group Residuals:
        Min          Q1         Med          Q3         Max 
-4.41570513 -0.43417155 -0.01997754  0.39562254  2.97768336 

Number of Observations: 492
Number of Groups: 82 

CCC estimated by variance compoments 
       CCC  LL CI 95%  UL CI 95%     SE CCC 
0.56588399 0.45925337 0.65643840 0.05031897 

cccrm documentation built on May 30, 2017, 3:39 a.m.