Flexible and Robust Agreement and Reliability Analyses.
SimplyAgree R Package
Artwork courtesy of Chelsea Parlett Pelleriti
Please see the package’s website for updates, vignettes, and other details about the package.
Background
SimplyAgree is an R package, and jamovi module, created to make agreement and reliability analyses easier for the average researcher. The functions within this package include simple tests of agreement (agree_test), agreement analysis for nested (agree_nest) and replicate data (agree_reps), and provide robust analyses of reliability (reli_stats). In addition, this package contains a set of functions to help when planning studies looking to assess measurement agreement (blandPowerCurve).
Installing SimplyAgree
You can install the most up-to-date version of SimplyAgree from GitHub with:
devtools::install_github("arcaldwell49/SimplyAgree")
Contributing
We are happy to receive bug reports, suggestions, questions, and (most of all) contributions to fix problems and add features. Pull Requests for contributions are encouraged.
Here are some simple ways in which you can contribute (in the increasing order of commitment):
- Read and correct any inconsistencies in the documentation
- Raise issues about bugs or wanted features
- Review code
- Add new functionality
Code of Conduct
Please note that the SimplyAgree project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
References
The functions in this package are largely based on the following works:
Lin L (1989). A concordance correlation coefficient to evaluate reproducibility. Biometrics 45: 255 - 268. https://doi.org/10.2307/2532051
Shieh, G. (2019). Assessing agreement between two methods of quantitative measurements: Exact test procedure and sample size calculation. Statistics in Biopharmaceutical Research, 1-8. https://doi.org/10.1080/19466315.2019.1677495
Parker, R. A., et al (2016). Application of mixed effects limits of agreement in the presence of multiple sources of variability: exemplar from the comparison of several devices to measure respiratory rate in COPD patients. Plos one, 11(12), e0168321. https://doi.org/10.1371/journal.pone.0168321
Zou, G. Y. (2013). Confidence interval estimation for the Bland–Altman limits of agreement with multiple observations per individual. Statistical methods in medical research, 22(6), 630-642. https://doi.org/10.1177/0962280211402548
Weir, J. P. (2005). Quantifying test-retest reliability using the intraclass correlation coefficient and the SEM. The Journal of Strength & Conditioning Research, 19(1), 231-240.
Lu, Meng-Jie, et al (2016). “Sample Size for Assessing Agreement between Two Methods of Measurement by Bland−Altman Method” The International Journal of Biostatistics, 12(2), https://doi.org/10.1515/ijb-2015-0039
King, TS and Chinchilli, VM. (2001). A generalized concordance correlation coefficient for continuous and categorical data. Statistics in Medicine, 20, 2131:2147.
King, TS, Chinchilli, VM, and Carrasco, JL. (2007). A repeated measures concordance correlation coefficient. Statistics in Medicine, 26, 3095:3113.
Carrasco, JL, et al. (2013). Estimation of the concordance correlation coefficient for repeated measures using SAS and R. Computer Methods and Programs in Biomedicine, 109, 293-304.