Next Article in Journal
Disposable and Low-Cost Colorimetric Sensors for Environmental Analysis
Next Article in Special Issue
Variability Matters
Previous Article in Journal
Long-Term Effects of a Cognitive Behavioral Conference Call Intervention on Depression in Non-Professional Caregivers
 
 
Article

Nonparametric Limits of Agreement in Method Comparison Studies: A Simulation Study on Extreme Quantile Estimation

by 1,2
1
Department of Nuclear Medicine, Odense University Hospital, 5000 Odense C, Denmark
2
Department of Clinical Research, University of Southern Denmark, 5000 Odense C, Denmark
Int. J. Environ. Res. Public Health 2020, 17(22), 8330; https://doi.org/10.3390/ijerph17228330
Received: 28 September 2020 / Revised: 6 November 2020 / Accepted: 9 November 2020 / Published: 11 November 2020
(This article belongs to the Special Issue Statistical and Epidemiological Methods in Public Health)
Bland–Altman limits of agreement and the underlying plot are a well-established means in method comparison studies on quantitative outcomes. Normally distributed paired differences, a constant bias, and variance homogeneity across the measurement range are implicit assumptions to this end. Whenever these assumptions are not fully met and cannot be remedied by an appropriate transformation of the data or the application of a regression approach, the 2.5% and 97.5% quantiles of the differences have to be estimated nonparametrically. Earlier, a simple Sample Quantile (SQ) estimator (a weighted average of the observations closest to the target quantile), the Harrell–Davis estimator (HD), and estimators of the Sfakianakis–Verginis type (SV) outperformed 10 other quantile estimators in terms of mean coverage for the next observation in a simulation study, based on sample sizes between 30 and 150. Here, we investigate the variability of the coverage probability of these three and another three promising nonparametric quantile estimators with n=50(50)200,250(250)1000. The SQ estimator outperformed the HD and SV estimators for n=50 and was slightly better for n=100, whereas the SQ, HD, and SV estimators performed identically well for n150. The similarity of the boxplots for the SQ estimator across both distributions and sample sizes was striking. View Full-Text
Keywords: agreement; Bland–Altman analysis; coverage; limits of agreement; method comparison; quantile estimation; repeatability; reproducibility agreement; Bland–Altman analysis; coverage; limits of agreement; method comparison; quantile estimation; repeatability; reproducibility
Show Figures

Figure 1

MDPI and ACS Style

Gerke, O. Nonparametric Limits of Agreement in Method Comparison Studies: A Simulation Study on Extreme Quantile Estimation. Int. J. Environ. Res. Public Health 2020, 17, 8330. https://doi.org/10.3390/ijerph17228330

AMA Style

Gerke O. Nonparametric Limits of Agreement in Method Comparison Studies: A Simulation Study on Extreme Quantile Estimation. International Journal of Environmental Research and Public Health. 2020; 17(22):8330. https://doi.org/10.3390/ijerph17228330

Chicago/Turabian Style

Gerke, Oke. 2020. "Nonparametric Limits of Agreement in Method Comparison Studies: A Simulation Study on Extreme Quantile Estimation" International Journal of Environmental Research and Public Health 17, no. 22: 8330. https://doi.org/10.3390/ijerph17228330

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop