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Vanbelle (2019) crackles lung-sound agreement study (vector-valued ratings)

Source: R/datasets.R
dat.vanbelle2019.Rd

The CRACKLES auscultation study: 20 patients from the Tromso cohort, each recorded at six chest sites, classified for the presence of crackles (`0`/`1`) by 28 observers from seven groups of four (international experts `EXP`, general practitioners from Norway `NOR`, Russia `RUS`, Wales `WAL` and the Netherlands `NLD`, pulmonologists `PUL`, and medical students `STU`). Because each observer rates a patient at all six sites, every rating is a six-component *vector*, which makes this the worked example for the vector-valued (component-separable) path of [kappa()]: pass the array and pick a component loss with `weight`. Ratings are complete (no missing entries). The data are factual binary classifications and are treated as public domain.

Usage

dat.vanbelle2019

Format

A 20 x 28 x 6 numeric array of `0`/`1` crackle classifications with named dimensions:

patient

20 patients (`"1"`–`"20"`).

rater

28 observers, four per group: `EXP1`–`EXP4`, `NOR1`–`NOR4`, `RUS1`–`RUS4`, `WAL1`–`WAL4`, `NLD1`–`NLD4`, `PUL1`–`PUL4`, `STU1`–`STU4`.

site

six chest sites: `U1`, `U2` (upper posterior, left/right), `L1`, `L2` (lower posterior), `A1`, `A2` (anterior).

Source

The `CRACKLES` data frame distributed with the multiagree package (Vanbelle), reshaped into a subjects-by-raters-by-features array.

References

Vanbelle, S. (2019). Asymptotic variability of (multilevel) multirater kappa coefficients. Statistical Methods in Medical Research, 28(10-11), 3012-3026. doi:10.1177/0962280218794733

Aviles-Solis, J. C., Vanbelle, S., Halvorsen, P. A., et al. (2017). International perception of lung sounds: a comparison of classification across some European borders. BMJ Open Respiratory Research, 4(1), e000250. doi:10.1136/bmjresp-2017-000250

Examples

# Vector-valued agreement across the six sites for the four expert observers,
# with the default Hamming component loss (count of disagreeing sites).
kappa(dat.vanbelle2019[, 1:4, ], estimator = "pairwise")
#> misskappa: estimator=pairwise, weight=hamming
#>        estimate     se  lower  upper
#> Conger   0.5192 0.0794 0.3635 0.6748
#> Fleiss   0.5150 0.0813 0.3557 0.6743