ccc_est_by_time: Concordance Correlation Coefficient estimation by variance...

In cccrm: Concordance Correlation Coefficient for Repeated (and Non-Repeated) Measures

Concordance Correlation Coefficient estimation by variance components.

Description

Estimation of the non-longitudinal concordance correlation coefficient at each time using the variance components approach.

Usage

ccc_est_by_time(
  dataset,
  ry,
  rind,
  rmet,
  rtime,
  covar = NULL,
  int = F,
  cl = 0.95,
  control.lme = list(),
  future_seed = TRUE,
  transf = "F2",
  workers = 15,
  plotit = TRUE,
  test = FALSE,
  nboot = 500,
  adj.method = "holm",
  ...
)

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.
rtime	Character string. Name of the time variable in the data set.
covar	Character vector. Name of covariates to include in the linear mixed model as fixed effects.
int	Binary indicating if the subject-method interaction has to be included in the model when analyzing the non-longitudinal setting (defaults to FALSE).
cl	Confidence level.
control.lme	A list of control values for the estimation algorithm used in lme function. For further details see lme help.
future_seed	Logical/Integer. The seed to be used for parallellization. Further details in furrr_options.
transf	Character string. Whether to apply a transformation of the coefficient for inference. Valid options are: "F" for Fisher's Z-transformation; "F2" For Fisher's Z-transformation setting m=2 (default); "KG" Konishi-Gupta transformation; "None", no transformation is applied. See *Details* for further information.
workers	Integer. Number of cores to be used for parallellization. Default is 15. Capped to number of available cores minus 1.
plotit	Logical. If TRUE it generates a plot with the CCC and their confidence intervals for each time.
test	Logical. If TRUE the equality of CCCs is assessed. Default to FALSE.
nboot	Number of bootstrap resamples.
adj.method	Character string. Correction method for pairwise comparisons. See p.adjust
...	To pass further arguments.

Details

The concordance correlation coefficient is estimated using the variance components approach. Confidence intervals are built using the asymptotic Normal distribution approach. Variance-covariance matrix of CCC estimates is estimated by non-parametric balanced randomized cluster bootstrap approach (Davison and Hinkley, 1997; Field and Welsh, 2007). Overall equality of CCCs is tested following the non-parametric bootstrap approach suggested in Vanbelle (2017).

Value

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

• ccc. CCC estimates at each level of time variable.

• plot. Plot of the CCC along with their confidence intervals.

• res_test. Test of equality of CCCs.

• ph_table. Pairwise comparison of CCCs.

References

Davison A.C., Hinkley D.V. (1997). Bootstrap Methods and Their Application. Cambridge: Cambridge University Press.

Field, C.A., Welsh, A.H. (2007). Bootstrapping Clustered Data. Journal of the Royal Statistical Society. Series B (Statistical Methodology) 69(3):369-390.

Vanbelle S. (2017). Comparing dependent kappa coefficients obtained on multilevel data. Biometrical Journal 59(5):1016-1034.

Examples

## Not run: 
ccc_est_by_time(bdaw, "AUC", "SUBJ", "MET", "VNUM")
ccc_est_by_time(bpres, "SIS", "ID", "METODE", "NM",test=TRUE)

## End(Not run)

cccrm documentation built on June 8, 2025, 11:23 a.m.