confIntICC: Confidence intervals for intraclass correlation in interrater...
In biostatUZH: Misc Tools of the Department of Biostatistics, EBPI, University of Zurich

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

Compute, under suitable assumptions, confidence intervals for interrater reliability for continuous measurements presented to two or more raters.

1	confIntICC(dat, conf.level = 0.95, psi.re.0 = c(0, 1))

`dat`	Data frame that contains the columns score, pat, rater.
`conf.level`	Confidence level for confidence interval.
`psi.re.0`	2-d vector specifying the interval [psi_0, psi_1] on p. 621 of Rousson et al. (2003).

This function computes all the confidence intervals that are discussed in Roussen et al. (2003). In applications, the interval under the "trained rater" assumptions is often suitable.

A list containing:

`icc(2, 1)`	ICC(2, 1): Intraclass correlation from a two-random effects model.
`icc(3, 1)`	ICC(3, 1): Intraclass correlation from a model with fixed rater effect.
`psi_r/e`	The value ψ_{r/e} computed from the actual data.
`ci.trained.rater`	Confidence interval under the trained rater assumption, see Rousson et al. (2003), Section 4.
`ci.low.asy.corr`	Lower bound of asymptotically exact confidence interval, see Rousson et al. (2003), Section 3.
`ci.low.fix.rater`	Lower bound of confidence interval under the fixed rater assumption, see Rousson et al. (2003), Section 5.

The function computeICCrater computes ICCs relying on a mixed-model formulation, and is therefore able to handle unbalanced data. On the contrary, the confidence intervals in the function confIntICC are computed using sums of squares, and the data must therefore be balanced. See the example below.

Kaspar Rufibach
kaspar.rufibach@gmail.com

Rousson, V., Gasser, T., and Seifert, B. (2002). Assessing intrarater, interrater and test-retest reliability of continuous measurements. Statist. Med., 21, 3431–3446.

Rousson, V., Gasser, T., and Seifert, B. (2003). Confidence intervals for intraclass correlation in inter-rater reliability. Scand. J. Statist., 30, 617–624.

computeICCrater

## Generate dataset. Data must be balanced!
set.seed(1977)
n <- 40
r1 <- round(runif(n, 1, 20))
dat <- data.frame(
    "score" = c(r1, r1 + abs(round(rnorm(n, 1, 3))), 
        r1 + abs(round(rnorm(n, 1, 3)))), 
    "pat" = rep(c(1:n), 3),
    "rater" = rep(1:3, each = n)
)
confIntICC(dat, conf.level = 0.95, psi.re.0 = c(0, 1))

if (requireNamespace("lme4")) {
    computeICCrater(dat)
}