Dataset Information

Bar charts enhance Bland-Altman plots when value ranges are limited.

ABSTRACT:

Objective

A common form of validation study compares alternative methods for collecting data. The Bland-Altman plot pairs observations across methods and plots their mean values vs. their difference. This method provides only limited information, however, when the range of observed values is small relative to the number of observations. This brief report shows how adding a simple bar chart to a Bland-Altman plot adds essential additional information.

Study design and setting

The methodological approach is illustrated using data from a randomized controlled clinical trial of patients in a U.S. county health system.

Results

When the number of unique values is small, a Bland-Altman plot alone may provide inadequate information. Adding a bar chart yields new and essential information about agreement, bias, and heteroscedasticity.

Conclusion

Studies validating one data-collection method against another can be performed successfully even when the number of unique values is small.

SUBMITTER: Smith MW

PROVIDER: S-EPMC2818014 | biostudies-literature | 2010 Feb

REPOSITORIES: biostudies-literature

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Publications

Bar charts enhance Bland-Altman plots when value ranges are limited.

Smith Mark W MW Ma Jun J Stafford Randall S RS

Journal of clinical epidemiology 20090827 2

<h4>Objective</h4>A common form of validation study compares alternative methods for collecting data. The Bland-Altman plot pairs observations across methods and plots their mean values vs. their difference. This method provides only limited information, however, when the range of observed values is small relative to the number of observations. This brief report shows how adding a simple bar chart to a Bland-Altman plot adds essential additional information.<h4>Study design and setting</h4>The m ...[more]

PMID: 19716265

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Project description:Reliable quantification in biological systems of endogenous low- and high-molecular substances, drugs and their metabolites, is of particular importance in diagnosis and therapy, and in basic and clinical research. The analytical characteristics of analytical approaches have many differences, including in core features such as accuracy, precision, specificity, and limits of detection (LOD) and quantitation (LOQ). Several different mathematic approaches were developed and used for the comparison of two analytical methods applied to the same chemical compound in the same biological sample. Generally, comparisons of results obtained by two analytical methods yields different quantitative results. Yet, which mathematical approach gives the most reliable results? Which mathematical approach is best suited to demonstrate agreement between the methods, or the superiority of an analytical method A over analytical method B? The simplest and most frequently used method of comparison is the linear regression analysis of data observed by method A (y) and the data observed by method B (x): y = α + βx. In 1986, Bland and Altman indicated that linear regression analysis, notably the use of the correlation coefficient, is inappropriate for method-comparison. Instead, Bland and Altman have suggested an alternative approach, which is generally known as the Bland-Altman approach. Originally, this method of comparison was applied in medicine, for instance, to measure blood pressure by two devices. The Bland-Altman approach was rapidly adapted in analytical chemistry and in clinical chemistry. To date, the approach suggested by Bland-Altman approach is one of the most widely used mathematical approaches for method-comparison. With about 37,000 citations, the original paper published in the journal The Lancet in 1986 is among the most frequently cited scientific papers in this area to date. Nevertheless, the Bland-Altman approach has not been really set on a quantitative basis. No criteria have been proposed thus far, in which the Bland-Altman approach can form the basis on which analytical agreement or the better analytical method can be demonstrated. In this article, the Bland-Altman approach is re-valuated from a quantitative bioanalytical perspective, and an attempt is made to propose acceptance criteria. For this purpose, different analytical methods were compared with Gold Standard analytical methods based on mass spectrometry (MS) and tandem mass spectrometry (MS/MS), i.e., GC-MS, GC-MS/MS, LC-MS and LC-MS/MS. Other chromatographic and non-chromatographic methods were also considered. The results for several different endogenous substances, including nitrate, anandamide, homoarginine, creatinine and malondialdehyde in human plasma, serum and urine are discussed. In addition to the Bland-Altman approach, linear regression analysis and the Oldham-Eksborg method-comparison approaches were used and compared. Special emphasis was given to the relation of difference and mean in the Bland-Altman approach. Currently available guidelines for method validation were also considered. Acceptance criteria for method agreement were proposed, including the slope and correlation coefficient in linear regression, and the coefficient of variation for the percentage difference in the Bland-Altman and Oldham-Eksborg approaches.

Project description:ObjectiveThis study aims to propose a comprehensive alternative to the Bland-Altman plot method, addressing its limitations and providing a statistical framework for evaluating the equivalences of measurement techniques. This involves introducing an innovative three-step approach for assessing accuracy, precision, and agreement between techniques, which enhances objectivity in equivalence assessment. Additionally, the development of an R package that is easy to use enables researchers to efficiently analyze and interpret technique equivalences.MethodsInferential statistics support for equivalence between measurement techniques was proposed in three nested tests. These were based on structural regressions with the goal to assess the equivalence of structural means (accuracy), the equivalence of structural variances (precision), and concordance with the structural bisector line (agreement in measurements obtained from the same subject), using analytical methods and robust approach by bootstrapping. To promote better understanding, graphical outputs following Bland and Altman's principles were also implemented.ResultsThe performance of this method was shown and confronted by five data sets from previously published articles that used Bland and Altman's method. One case demonstrated strict equivalence, three cases showed partial equivalence, and one showed poor equivalence. The developed R package containing open codes and data are available for free and with installation instructions at Harvard Dataverse at https://doi.org/10.7910/DVN/AGJPZH.ConclusionAlthough easy to communicate, the widely cited and applied Bland and Altman plot method is often misinterpreted, since it lacks suitable inferential statistical support. Common alternatives, such as Pearson's correlation or ordinal least-square linear regression, also fail to locate the weakness of each measurement technique. It may be possible to test whether two techniques have full equivalence by preserving graphical communication, in accordance with Bland and Altman's principles, but also adding robust and suitable inferential statistics. Decomposing equivalence into three features (accuracy, precision, and agreement) helps to locate the sources of the problem when fixing a new technique.

Project description:BACKGROUND/OBJECTIVES:Bland-Altman methods for assessing the agreement between two measures are highly cited. However, these methods may often not be used to assess agreement, and when used, they are not always presented or interpreted correctly. Our objective was to evaluate the use and the quality of reporting of Bland-Altman analyses in studies that compare self-reported with measured weight and height. METHODS:We evaluated the use of Bland-Altman methods in 394 published articles that compared self-reported and measured weight and height data for adolescents or adults. Six reporting criteria were developed: assessment of the normality of the distribution of differences, a complete and correctly labeled Bland-Altman plot displaying the mean difference and limits of agreement (LOA), numerical values and confidence intervals, standard errors, or standard deviations for mean difference, numerical values of LOA, confidence intervals for LOA, and prespecified criteria for acceptable LOA. RESULTS:Only 72/394 (18%) studies comparing self-reported with measured weight and height or BMI used some form of Bland-Altman analyses. No study using Bland-Altman analyses satisfied more than four of the six criteria. Of the 72 studies, 64 gave mean differences along with confidence intervals or standard deviations, 55 provided complete Bland-Altman plots that were appropriately labeled and described, 37 provided numerical values for LOA, 4 reported that they examined the normality of the distribution of differences, 3 provided confidence intervals for LOA, and 3 had prespecified criteria for agreement. CONCLUSIONS:Bland-Altman methods appear to be infrequently used in studies comparing measured with self-reported weight, height, or BMI, and key information is missing in many of those that do use Bland-Altman methods. Future directions would be defining acceptable LOA values and improving the reporting and application of Bland-Altman methods in studies of self-reported anthropometry.

Dataset Information

Bar charts enhance Bland-Altman plots when value ranges are limited.

Objective

Study design and setting

Results

Conclusion

Publications

Bar charts enhance Bland-Altman plots when value ranges are limited.

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