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 1: 6.1-6.13. doi:10.1186/1471-2288-9-6

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   Check publisher's open access policy
ISSN 1471-2288
Publication date 2009-01-22
Sub-type Article (original research)
DOI 10.1186/1471-2288-9-6
Volume 9
Issue 1
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.

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).

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.

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.
References 1. de Vet HC, Terwee CB, Bouter LM: Current challenges in clinimetrics. J Clin Epidemiol 2003, 56:1137-1141. 2. Rothman KJ, Greenland S: Modern Epidemiology 2nd edition. Philadelphia: Lippincott-Raven; 1998. 3. Luiz RR, Szklo M: More than one statistical strategy to assess agreement of quantitative measurements may usefully be reported. J Clin Epidemiol 2005, 58:215-216. 4. Bland JM, Altman DG: Applying the right statistics: analyses of measurement studies. Ultrasound Obstet Gynecol 2003, 22:85-93. 5. Bland JM, Altman DG: Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986, 1:307-310. 6. Bland JM, Altman DG: Measuring agreement in method comparison studies. Stat Methods Med Res 1999, 8:135-160. 7. Ludbrook J: Statistical techniques for comparing measurers and methods of measurement: a critical review. Clin Exp Pharmacol Physiol 2002, 29:527-536. 8. Carstensen B: Comparing and predicting between several methods of measurement. Biostatistics 2004, 5:399-413. 9. Luiz RR, Costa AJ, Kale PL, Werneck GL: Assessment of agreement of a quantitative variable: a new graphical approach. J Clin Epidemiol 2003, 56:963-967. 10. White SA, Broek NR van den: Methods for assessing reliability and validity for a measurement tool: a case study and critique using the WHO haemoglobin colour scale. Stat Med 2004, 23:1603-1619. 11. Carstensen B, Gurrin L, Simpson J: Comparing and predicting between measurement methods. 2006 [http://staff.pubhealth.ku.dk/~bxc/]. Melbourne: Royal Children's Hospital 12. Congdon P: Bayesian Statistical Modelling Chichester: Wiley; 2002. 13. Berger JO: Statistical Decision Theory and Bayesian Analysis 2nd edition. New York: Springer-Verlag; 1985. 14. Gelman A, Carlin JB, Stern HS, Rubin DB: Bayesian Data Analysis London: Chapman & Hall; 1997. 15. Oliver M, Schofield GM, Kolt GS, Schluter PJ: Pedometer accuracy in physical activity assessment of preschool children. J Sci Med Sport 2007, 10:303-310. 16. Lunn DJ, Thomas A, Best N, Spiegelhalter D: WinBUGS – a Bayesian modelling framework: concepts, structure, and extensibility. Stat Comput 2000, 10:325-337. 17. Stata Corporation: Intercooled Stata 8.0 for Windows. 80th edition. College Station, TX: Stata Corporation; 2003. 18. Brooks SP, Gelman A: Alternative methods for monitoring convergence of iterative simulations. J Comput Graph Stat 1998, 7:434-455. 19. Lu G, Ades AE: Combination of direct and indirect evidence in mixed treatment comparisons. Stat Med 2004, 23:3105-3124. 20. Woolrich MW, Behrens TE, Beckmann CF, Jenkinson M, Smith SM: Multilevel linear modelling for FMRI group analysis using Bayesian inference. Neuroimage 2004, 21:1732-1747. 21. Goldstein H, Browne W, Rasbash J: Multilevel modelling of medical data. Stat Med 2002, 21:3291-3315.
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Article number 6

Document type: Journal Article
Sub-type: Article (original research)
Collections: 2010 Higher Education Research Data Collection
ERA 2012 Admin Only
School of Nursing, Midwifery and Social Work Publications
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 5 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 6 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Access Statistics: 4817 Abstract Views  -  Detailed Statistics
Created: Tue, 25 Aug 2009, 14:30:40 EST by Vicki Percival on behalf of School of Nursing, Midwifery and Social Work