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kappaGold

The R-package kappaGold is about agreement of nominal scale raters. You can find various agreement measures (like Fleiss’ kappa) but the main focus of this package is on agreement where there is a gold standard.

Gold standard

Sometimes, not all raters have equal status but there is an instance considered to speak the ground truth and we might want to estimate the agreement of the other raters with this gold standard.

Single gold standard rater

For a single gold standard rater we build on the idea of Conger that the multi-rater kappa due to Light (1971) is actually a mean of all pairwise Cohen’s kappas. In the situation of a gold standard, we only consider the pairwise Cohen’s kappas of each rater with that gold standard.

The implementation of this measure of agreement with a gold standard is found in the function kappam_gold. kappam_gold expects a matrix of ratings with observations in the row and raters in the columns. The gold standard rater is by default taken from the 1st column. The delete-1 jackknife method is used to get an estimate of bias and standard error.

Example

In medicine, staging is the process of assessing the extent to which a tumour has grown. Staging affets treatment choice, for instance, if radiation is used or not. Pathological assessment is typically the gold-standard while non-invasive imaging allows for easier and earlier tumour staging by radiologists. Inspired by the OCUM-trial on colorectal tumour staging the data set stagingData carries the fictitious staging of 21 colorectal tumour patients by a pathologist based on a histological sample (gold standard) and 5 different radiologists. The agreement of the radiologists (columns 2 to 6) with the pathological staging as gold standard can be estimated by kappam_gold:

library("kappaGold")

# 1st column corresponds to gold-standard
kappam_gold(kappaGold::stagingData)
#> $method
#> [1] "Averaged Cohen's Kappa with gold standard"
#> 
#> $subjects
#> [1] 21
#> 
#> $raters
#> [1] 5
#> 
#> $categories
#> [1] 3
#> 
#> $agreem
#> [1] 0.60952
#> 
#> $value0
#> [1] 0.41429
#> 
#> $value
#> [1] 0.42552
#> 
#> $se_j
#> [1] 0.074303
#> 
#> $conf.level
#> [1] 0.95
#> 
#> $ci.lo
#> [1] 0.27989
#> 
#> $ci.hi
#> [1] 0.57115
#> 
#> $ci.width
#> [1] 0.29126

Entry agreem is the mean pairwise agreement between the raters (to be evaluated) and the gold standard rating. The entry value0 shows the mean of all pairwise Cohen’s kappa between the raters and the gold standard. Delete-1 jackknife gives an estimate for bias and standard error. These quantities are used to get the bias-corrected estimate value which can be used as point estimate and a 95% confidence interval.

Multiple gold standard raters

The situation is more complicated when there are multiple gold standard rater. Often this is necessary when the rating is difficult, maybe because the rating scale is not well defined and the only gold standard is the consensus of a group a experienced raters. For this situation, the package kappaGold has an implementation of an agreement coefficient proposed by Vanbelle, S. and Albert, A. Agreement between Two Independent Groups of Raters. Psychometrika 74, 477–491 (2009) https://doi.org/10.1007/s11336-009-9116-1.

Note that this method applies for any two groups of raters, i.e., it does not need the notion of gold standard here.

Inference on kappa

Besides estimating agreement, we might need to compare the agreement between two groups. In KappaGold, you can find the methods for two cases:

Installation

Package kappaGold was initially released to CRAN in September 2024 (v0.3.2). To install the current version, simply issue install.packages("kappaGold") in your R-session. The development of the R-package kappaGold is going on at Gitlab. With the help of the remotes-package you can install the development version of package kappaGold via:

remotes::install_gitlab("imb-dev/kappa_gold@develop")

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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