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

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.

1 |

`dataset` |
an object of class |

`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. |

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.

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 |

Josep Puig-Martinez and Josep L. Carrasco

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.

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```
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
```

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