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Bland–Altman Limits of Agreement from a Bayesian and Frequentist Perspective

Author

Listed:
  • Oke Gerke

    (Department of Nuclear Medicine, Odense University Hospital, 5000 Odense, Denmark
    Department of Clinical Research, University of Southern Denmark, 5000 Odense, Denmark)

  • Sören Möller

    (Department of Clinical Research, University of Southern Denmark, 5000 Odense, Denmark
    Open Patient Data Explorative Network, Odense University Hospital, 5000 Odense, Denmark)

Abstract

Bland–Altman agreement analysis has gained widespread application across disciplines, last but not least in health sciences, since its inception in the 1980s. Bayesian analysis has been on the rise due to increased computational power over time, and Alari, Kim, and Wand have put Bland–Altman Limits of Agreement in a Bayesian framework (Meas. Phys. Educ. Exerc. Sci. 2021, 25, 137–148). We contrasted the prediction of a single future observation and the estimation of the Limits of Agreement from the frequentist and a Bayesian perspective by analyzing interrater data of two sequentially conducted, preclinical studies. The estimation of the Limits of Agreement θ 1 and θ 2 has wider applicability than the prediction of single future differences. While a frequentist confidence interval represents a range of nonrejectable values for null hypothesis significance testing of H 0 : θ 1 ≤ −δ or θ 2 ≥ δ against H 1 : θ 1 > −δ and θ 2 < δ, with a predefined benchmark value δ, Bayesian analysis allows for direct interpretation of both the posterior probability of the alternative hypothesis and the likelihood of parameter values. We discuss group-sequential testing and nonparametric alternatives briefly. Frequentist simplicity does not beat Bayesian interpretability due to improved computational resources, but the elicitation and implementation of prior information demand caution. Accounting for clustered data (e.g., repeated measurements per subject) is well-established in frequentist, but not yet in Bayesian Bland–Altman analysis.

Suggested Citation

  • Oke Gerke & Sören Möller, 2021. "Bland–Altman Limits of Agreement from a Bayesian and Frequentist Perspective," Stats, MDPI, vol. 4(4), pages 1-11, December.
  • Handle: RePEc:gam:jstats:v:4:y:2021:i:4:p:62-1090:d:705850
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    References listed on IDEAS

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    1. Oke Gerke, 2020. "Nonparametric Limits of Agreement in Method Comparison Studies: A Simulation Study on Extreme Quantile Estimation," IJERPH, MDPI, vol. 17(22), pages 1-14, November.
    2. Maria E. Frey & Hans C. Petersen & Oke Gerke, 2020. "Nonparametric Limits of Agreement for Small to Moderate Sample Sizes: A Simulation Study," Stats, MDPI, vol. 3(3), pages 1-13, August.
    3. Patrick Taffé & Mingkai Peng & Vicki Stagg & Tyler Williamson, 2017. "biasplot: A package to effective plots to assess bias and precision in method comparison studies," Stata Journal, StataCorp LP, vol. 17(1), pages 208-221, March.
    4. Christopher Jennison & Bruce W. Turnbull, 2006. "Adaptive and nonadaptive group sequential tests," Biometrika, Biometrika Trust, vol. 93(1), pages 1-21, March.
    5. Ghosal,Subhashis & van der Vaart,Aad, 2017. "Fundamentals of Nonparametric Bayesian Inference," Cambridge Books, Cambridge University Press, number 9780521878265, October.
    Full references (including those not matched with items on IDEAS)

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