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Clinical Chemistry 50: 2216-2218, 2004; 10.1373/clinchem.2004.036095
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(Clinical Chemistry. 2004;50:2216-2218.)
© 2004 American Association for Clinical Chemistry, Inc.


Letters to the Editor

Interpreting Method Comparison Studies by Use of the Bland–Altman Plot: Reflecting the Importance of Sample Size by Incorporating Confidence Limits and Predefined Error Limits in the Graphic

Dietmar Stöckl1, Diego Rodríguez Cabaleiro2, Katleen Van Uytfanghe2 and Linda M. Thienpont2,a

1 STT Consulting, Horebeke, Belgium
2 Laboratorium voor Analytische Chemie, Faculteit Farmaceutische Wetenschappen, Universiteit Gent, Gent, Belgium

aAddress correspondence to this author at: Laboratorium voor Analytische Chemie, Faculteit Farmaceutische Wetenschappen, Universiteit Gent, Harelbekestraat 72, B-9000 Gent, Belgium. Fax 32-9-264-81-98; e-mail linda.thienpont{at}ugent.be.


To the Editor:

The difference, or Bland–Altman plot (1)(2)(3)(4) has become a popular tool for the presentation of method-comparison studies (5)(6), but the plot has rarely been used for making decisions about the quality of a method (5)(6). Bland and Altman expressed this in the terms "we want to know by how much the new method is likely to differ from the old; if this is not enough to cause problems in clinical interpretation we can replace the old method by the new or use the two interchangeably. How far apart measurements can be without causing difficulties will be a question of judgment. Ideally, it should be defined in advance to help in the interpretation of the method comparison and to choose the sample size" (2). The tool for doing so was to investigate whether the upper (UCL) or lower (LCL) 95% confidence limit of 1.96 SD of the differences between the methods (UCL1.96 SD,diff, LCL1.96 SD,diff) was equal to or smaller than a predefined limit for total error (TE; acceptance, UCL1.96 SD,diff or LCL1.96 SD,diff ≤ TE). The reason that this strategy is seldom applied may be that Bland and Altman presented neither the acceptance limits nor the confidence intervals (CIs) in the graphic (1)(2)(3)(4), whereas in fact, they discussed the "precision of the estimates" (CId, CI1.96 SD,diff, where d is the mean difference) and their relationship with the sample size in detail (2). Moreover, based on UCL1.96 SD,diff or LCL1.96 SD,diff and a predefined medical limit, Bland and Altman made the decision that "the degree of agreement between the two [Wright] meters was not acceptable" (2). As an expansion of the concept, we propose to additionally investigate the question of whether the 95% confidence limits of d between the methods is equal to or smaller than a predefined limit for systematic error (SE; acceptance, UCLd or LCLd ≤ SE). Note that CId = ± t(95%, n – 1) x (SD,diff/{surd}n) and CI1.96 SD,diff = ± 1.71x t(95%, n – 1) x (SD,diff/{surd}n). Note also that we use the one-sided t values for the calculation of the CI because the question is whether the UCL or LCL is equal to or less than a predefined error limit.

Because of this unrecognized but important purpose of the difference plot, we present here a "model Bland–Altman plot" to be used for the interpretation of a method-comparison study vs predefined error limits (Fig. 1 ). The plot shows ±SE, ±TE, CId, and CI1.96 SD,diff in addition to the commonly presented lines representing d and d ± 1.96 SD,diff. At the same time, we emphasize the importance of the sample size (n) on the "acceptance decision" by simulations with sample sizes of 80 (Fig. 1A ), 40 (Fig. 1B ), and 20 (Fig. 1C ). We chose as example a simulated method comparison for serum cholesterol between an accuracy-based reference method (applying isotope-dilution gas chromatography/mass spectrometry) and a routine method. However, the simulation truly mimics a "real world" comparison that has been done before (7). The y axis represents the differences of the routine method from the reference method, expressed in percentage of the values of the reference method. This approach is recommended for data that span a "medium" range, where a more or less constant CV can be expected (8). Moreover, the 1.96 SD,diff deviations can directly be related to the CV of the routine method. The bias of the routine method was assumed to be 2.3% and the CV to be 3%. We used as acceptance limits for the routine method SE = 3% (9) and TE = 10%(10). For an overview about strategies for setting quality specifications, the reader is referred to a recent conference report (11).



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Figure 1. Bland–Altman plots of a simulated method comparison for serum cholesterol between isotope-dilution gas chromatography/mass spectrometry (ID-GC/MS) and a routine method.

The bias of the routine method was assumed to be 2.3% and the CV to be 3%. Acceptance limits for the routine method are SE = 3% (9) and TE = 10%(10).

Visual interpretation of Fig. 1AUp (n = 80) easily allows one to conclude that the routine method satisfies the limits for SE as well as TE (UCLd ≤ SE and UCL1.96 SD,diff ≤ TE). From Fig. 1BUp (n = 40), on the other hand, the conclusion would be that the routine method does not satisfy the SE limit (UCLd > SE), but does satisfy the TE limit (UCL1.96 SD,diff ≤ TE). From Fig. 1C (n = 20), one would conclude that the routine method satisfies neither the SE nor the TE limit (UCLd > SE and UCL1.96 SD,diff > TE).

In summary, the example demonstrates that the incorporation of confidence limits and predefined error limits in a Bland–Altman plot allows easy visual interpretation of a method-comparison study. Moreover, the confidence limits directly show the importance of the sample size for decisions about method acceptance, a fact that is usually not considered.

Finally, we want to remark that the confidence intervals (and indeed, limits of agreement) are by convention set at 95% but that other values might be used. Most obviously, one might in some situations require 99% limits of agreement to meet a predefined specification.


References

  1. Altman DG, Bland JM. Measurement in medicine: the analysis of method comparison studies. Statistician 1983;32:307-317.[CrossRef][ISI]
  2. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986;i:307-310.
  3. Bland JM, Altman DG. Comparing methods of measurement: why plotting difference against standard method is misleading. Lancet 1995;346:1085-1087.[CrossRef][ISI][Medline] [Order article via Infotrieve]
  4. Bland JM, Altman DG. Measuring agreement in method comparison studies. Stat Methods Med Res 1999;8:135-160.[Abstract/Free Full Text]
  5. Dewitte K, Fierens C, Stöckl D, Thienpont LM. Application of the Bland-Altman plot for the interpretation of method-comparison studies: a critical investigation of its practice. Clin Chem 2002;48:799-801.[Free Full Text]
  6. Mantha S, Roizen MF, Fleisher LA, Thisted R, Foss J. Comparing methods of clinical measurement: reporting standards for Bland and Altman analysis. Anesth Analg 2000;90:593-602.[Abstract/Free Full Text]
  7. Thienpont LM, Van Landuyt KG, Stockl D, De Leenheer AP. Four frequently used test systems for serum cholesterol evaluated by isotope dilution gas chromatography-mass spectrometry candidate reference method. Clin Chem 1996;42:531-535.[Abstract/Free Full Text]
  8. Pollock MA, Jefferson SG, Kane JW, Lomax K, MacKinnon G, Winnard CB. Method comparison—a different approach. Ann Clin Biochem 1992;29:556-560.
  9. . National Cholesterol Education Program. Recommendations for improving cholesterol measurements. US Department of Health and Human Services publication NIH 90-2964 1990 National Institutes of Health Washington, DC. .
  10. Clinical Laboratory Improvement Amendments of 1988; final rule. Fed Regist 1992;57:7001-7288.
  11. Petersen PH, Fraser CG, Kallner A, Kenny D. Strategies to set global quality specifications in laboratory medicine. Scand J Clin Lab Invest 1999;59:475-585.[CrossRef][ISI][Medline] [Order article via Infotrieve]



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