A multivariate hierarchical Bayesian approach to measuring agreement in repeated measurement method comparison studies

Schluter, Philip J. (2009) A multivariate hierarchical Bayesian approach to measuring agreement in repeated measurement method comparison studies. BMC Medical Research Methodology, 9 : 6.1-6.13.


Author Schluter, Philip J.
Title A multivariate hierarchical Bayesian approach to measuring agreement in repeated measurement method comparison studies
Journal name BMC Medical Research Methodology  (ERA 2012 Listed)    (ERA 2010 Rank C)   Check publisher's open access policy
Publication date 2009-01-22
Sub-type Article
DOI 10.1186/1471-2288-9-6
Volume number 9
ISSN 1471-2288
Start page 6.1
End page 6.13
Total pages 13
Editor Dr Melissa Norton
Place of publication London, United Kingdom
Publisher BioMed Central
Collection year 2010
Language eng
Subject C1
010402 Biostatistics
111706 Epidemiology
920499 Public Health (excl. Specific Population Health) not elsewhere classified
Formatted abstract Background: Assessing agreement in method comparison studies depends on two fundamentally important components; validity (the between method agreement) and reproducibility (the within method agreement). The Bland-Altman limits of agreement technique is one of the favoured approaches in medical literature for assessing between method validity. However, few researchers have adopted this approach for the assessment of both validity and reproducibility. This may be partly due to a lack of a flexible, easily implemented and readily available statistical machinery to analyse repeated measurement method comparison data.

Methods: Adopting the Bland-Altman framework, but using Bayesian methods, we present this statistical machinery. Two multivariate hierarchical Bayesian models are advocated, one which assumes that the underlying values for subjects remain static (exchangeable replicates) and one which assumes that the underlying values can change between repeated measurements (nonexchangeable replicates).

Results: We illustrate the salient advantages of these models using two separate datasets that have been previously analysed and presented; (i) assuming static underlying values analysed using both multivariate hierarchical Bayesian models, and (ii) assuming each subject's underlying value is continually changing quantity and analysed using the non-exchangeable replicate multivariate hierarchical Bayesian model.

Conclusion: These easily implemented models allow for full parameter uncertainty, simultaneous method comparison, handle unbalanced or missing data, and provide estimates and credible regions for all the parameters of interest. Computer code for the analyses in also presented, provided in the freely available and currently cost free software package WinBUGS.
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Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Article number 6

 
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Created: Tue, 25 Aug 2009, 14:30:40 EST by Vicki Percival on behalf of School of Nursing and Midwifery