A Robust Bayesian Estimate of the Concordance Correlation Coefficient

REFERENCES

• Barnett, V., Lewis, T. (1984). Outliers in Statistical Data. 2nd ed. Chichester: Wiley.
   Google Scholar
• Barnhart, H. X., Haber, M. J., Lin, L. I. (2007). An overview on assessing agreement with continuous measurements. Journal of Biopharmaceutical Statistics 17:529–569.
   PubMed Web of Science ®Google Scholar
• Berger, J. O. (1994). An overview of robust Bayesian analysis (with discussion). Test 3:5–124.
   Google Scholar
• Carlin, B. P., Louis, T. A. (2011). Bayesian Methods for Data Analysis. 3rd ed. Boca Raton, FL: CRC Press.
   Google Scholar
• Carpenter, J., Bithell, J. (2000). Bootstrap confidence intervals: When, which, what? A practical guide for medical statisticians. Statistics in Medicine 19:1141–1164.
   PubMed Web of Science ®Google Scholar
• Carrasco, J. L., Jover, L. (2003). Estimating the generalized concordance correlation coefficient through variance components. Biometrics 59:849–858.
   PubMed Web of Science ®Google Scholar
• Carrasco, J. L., Jover, L., King, T. S., Chinchilli, V. M. (2007). Comparison of concordance correlation coefficient estimating approaches with skewed data. Journal of Biopharmaceutical Statistics 17:673–684.
   PubMed Web of Science ®Google Scholar
• Cowles, M. K., Carlin, B. P. (1996). Markov chain Monte Carlo convergence diagnostics: A comparative review. Journal of the American Statistical Association 91:883–904.
   Web of Science ®Google Scholar
• Efron, B., Tibshirani, R. J. (1993). An Introduction to the Bootstrap. San Francisco, CA: Chapman & Hall.
   Google Scholar
• Feng, D. (2014). agRee: Various Methods for Measuring Agreement. Available at: http://cran.r-project.org/web/packages/agRee/.
   Google Scholar
• Feng, D., Svetnik, V., Coimbra, A., Baumgartner, R. (2014). A comparison of confidence interval methods for the concordance correlation coefficient and intraclass correlation coefficient with small number of raters. Journal of Biopharmaceutical Statistics 24(2):272–293.
   PubMed Web of Science ®Google Scholar
• Hampel, F. R., Ronchetti, E. M., Rousseeuw, P. J., Stahel, W. A. (1986). Robust Statistics: The Approach Based on Influence Functions. New York: Wiley.
   Google Scholar
• Jeffreys, H. (1957). Scientific Inference. Cambridge: Cambridge University Press.
   Google Scholar
• Kadane, J. B., Dickey, J. M., Winkler, R. L., Smith, W. S., Peters, S. C. (1980). Interactive elicitation of opinion for a normal linear model. Journal of the American Statistical Association 75:845–854.
   Web of Science ®Google Scholar
• King, T. S., Chinchilli, V. M. (2001). Robust estimators of the concordance correlation coefficient. Journal of Biopharmaceutical Statistics 11:83–105.
   PubMedGoogle Scholar
• Kotz, S., Nadarajah, S. (2004). Multivariate t Distributions and Their Applications. Cambridge: Cambridge University Press.
   Google Scholar
• Lin, L. (2008). Overview of agreement statistics for medical devices. Journal of Biopharmaceutical Statistics 18:126–144.
   PubMed Web of Science ®Google Scholar
• Lin, L. I., Hedayat, A. S., Wu, W. (2007). A unified approach for assessing agreement for continuous and categorical data. Journal of Biopharmaceutical Statistics a Special Issue on Agreement 17:629–652.
   PubMed Web of Science ®Google Scholar
• Lin, L. I.-K. (1989). Concordance correlation coefficient to evaluate reproducibility. Biometrics 45:225–268.
   Web of Science ®Google Scholar
• Liu, C. (1994). Statistical Analysis Using the Multivariate t Distribution. PhD Dissertation, Harvard University.
   Google Scholar
• Liu, C., Rubin, D. B. (1994). The ECME algorithm: A simple extension of EM and ECM with faster monotone convergence. Biometrika 81:633–648.
   Web of Science ®Google Scholar
• Liu, X., Du, Y., Teresi, J., Hasin, D. S. (2005). Concordance correlation in the measurements of time to event. Statistics in Medicine 24:1409–1420.
   PubMed Web of Science ®Google Scholar
• McBride, G. B. (2005). A Proposal for Strength-Of-Agreement Criteria for Lin’s Concordance Correlation Coefficient. NIWA Client Report: HAM2005-062. Report to Ministry of Health.
   Google Scholar
• Neal, R. M. (2003). Slice Sampling. The Annals of Statistics 31:705–741.
   Web of Science ®Google Scholar
• R Core Team (2014). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Available at: http://www.R-project.org/.
   Google Scholar
• Royston, J. P. (1982). An extension of Shapiro and Wilk’s W test for normality to large samples. Applied Statistics 31:115–124.
   Web of Science ®Google Scholar
• Svetnik, V., Ma, J., Soper, K. A., Doran, S., Renger, J. J., Deacon, S., Koblan, K. S. (2007). Evaluation of automated and semi-automated scoring of polysomnographic recordings from a clinical trial using zolpidem in the treatment of insomnia. Sleep 30:1562–1574.
   PubMed Web of Science ®Google Scholar
• Van Dyk, D. A., Meng, X. (2001). The art of data augmentation. Journal of Computational and Graphical Statistics 10:1–50.
   Web of Science ®Google Scholar
• Wakefield, J. (1996). The Bayesian analysis of population pharmacokinetic models. Journal of the American Statistical Association 91:62–75.
   Web of Science ®Google Scholar