An Extension of the Bland-Altman Plot for Analyzing the Agreement of More than Two Raters.
Ontology highlight
ABSTRACT:
SUBMITTER: Moller S
PROVIDER: S-EPMC7824071 | biostudies-literature | 2021 Jan
REPOSITORIES: biostudies-literature
Publications
An Extension of the Bland-Altman Plot for Analyzing the Agreement of More than Two Raters.
Möller Sören S Debrabant Birgit B Halekoh Ulrich U Petersen Andreas Kristian AK Gerke Oke O
Diagnostics (Basel, Switzerland) 20210101 1
The Bland-Altman plot is the most common method to analyze and visualize agreement between raters or methods of quantitative outcomes in health research. While very useful for studies with two raters, a limitation of the classical Bland-Altman plot is that it is specifically used for studies with two raters. We propose an extension of the Bland-Altman plot suitable for more than two raters and derive the approximate limits of agreement with 95% confidence intervals. We validated the suggested li ...[more]
Similar Datasets
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.
| S-EPMC10878685 | biostudies-literature
Project description:The Bland-Altman Limits of Agreement is a popular and widespread means of analyzing the agreement of two methods, instruments, or raters in quantitative outcomes. An agreement analysis could be reported as a stand-alone research article but it is more often conducted as a minor quality assurance project in a subgroup of patients, as a part of a larger diagnostic accuracy study, clinical trial, or epidemiological survey. Consequently, such an analysis is often limited to brief descriptions in the main report. Therefore, in several medical fields, it has been recommended to report specific items related to the Bland-Altman analysis. The present study aimed to identify the most comprehensive and appropriate list of items for such an analysis. Seven proposals were identified from a MEDLINE/PubMed search, three of which were derived by reviewing anesthesia journals. Broad consensus was seen for the a priori establishment of acceptability benchmarks, estimation of repeatability of measurements, description of the data structure, visual assessment of the normality and homogeneity assumption, and plotting and numerically reporting both bias and the Bland-Altman Limits of Agreement, including respective 95% confidence intervals. Abu-Arafeh et al. provided the most comprehensive and prudent list, identifying 13 key items for reporting (Br. J. Anaesth. 2016, 117, 569-575). An exemplification with interrater data from a local study accentuated the straightforwardness of transparent reporting of the Bland-Altman analysis. The 13 key items should be applied by researchers, journal editors, and reviewers in the future, to increase the quality of reporting Bland-Altman agreement analyses.
| S-EPMC7278016 | biostudies-literature
Project description:Bland-Altman method comparison studies are common in the medical sciences and are used to compare a new measure to a gold-standard (often costlier or more invasive) measure. The distribution of these differences is summarized by two statistics, the 'bias' and standard deviation, and these measures are combined to provide estimates of the limits of agreement (LoA). When these LoA are within the bounds of clinically insignificant differences, the new non-invasive measure is preferred. Very often, multiple Bland-Altman studies have been conducted comparing the same two measures, and random-effects meta-analysis provides a means to pool these estimates. We provide a framework for the meta-analysis of Bland-Altman studies, including methods for estimating the LoA and measures of uncertainty (i.e., confidence intervals). Importantly, these LoA are likely to be wider than those typically reported in Bland-Altman meta-analyses. Frequently, Bland-Altman studies report results based on repeated measures designs but do not properly adjust for this design in the analysis. Meta-analyses of Bland-Altman studies frequently exclude these studies for this reason. We provide a meta-analytic approach that allows inclusion of estimates from these studies. This includes adjustments to the estimate of the standard deviation and a method for pooling the estimates based upon robust variance estimation. An example is included based on a previously published meta-analysis. Copyright © 2017 John Wiley & Sons, Ltd.
| S-EPMC5585060 | biostudies-literature
Project description:BackgroundTo assess the agreement of continuous measurements between a number of observers, Jones et al. introduced limits of agreement with the mean (LOAM) for multiple observers, representing how much an individual observer can deviate from the mean measurement of all observers. Besides the graphical visualisation of LOAM, suggested by Jones et al., it is desirable to supply LOAM with confidence intervals and to extend the method to the case of multiple measurements per observer.MethodsWe reformulate LOAM under the assumption the measurements follow an additive two-way random effects model. Assuming this model, we provide estimates and confidence intervals for the proposed LOAM. Further, this approach is easily extended to the case of multiple measurements per observer.ResultsThe proposed method is applied on two data sets to illustrate its use. Specifically, we consider agreement between measurements regarding tumour size and aortic diameter. For the latter study, three measurement methods are considered.ConclusionsThe proposed LOAM and the associated confidence intervals are useful for assessing agreement between continuous measurements.
| S-EPMC7730774 | biostudies-literature
Project description:There are two schools of thought in statistical analysis, frequentist, and Bayesian. Though the two approaches produce similar estimations and predictions in large-sample studies, their interpretations are different. Bland Altman analysis is a statistical method that is widely used for comparing two methods of measurement. It was originally proposed under a frequentist framework, and it has not been used under a Bayesian framework despite the growing popularity of Bayesian analysis. It seems that the mathematical and computational complexity narrows access to Bayesian Bland Altman analysis. In this article, we provide a tutorial of Bayesian Bland Altman analysis. One approach we suggest is to address the objective of Bland Altman analysis via the posterior predictive distribution. We can estimate the probability of an acceptable degree of disagreement (fixed a priori) for the difference between two future measurements. To ease mathematical and computational complexity, an interface applet is provided with a guideline.
| S-EPMC8133695 | biostudies-literature
Project description:ObjectiveA 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 settingThe methodological approach is illustrated using data from a randomized controlled clinical trial of patients in a U.S. county health system.ResultsWhen 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.ConclusionStudies validating one data-collection method against another can be performed successfully even when the number of unique values is small.
| S-EPMC2818014 | biostudies-literature
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.
| S-EPMC10343610 | biostudies-literature
Project description:Screening and diagnostic procedures often require a physician's subjective interpretation of a patient's test result using an ordered categorical scale to define the patient's disease severity. Because of wide variability observed between physicians' ratings, many large-scale studies have been conducted to quantify agreement between multiple experts' ordinal classifications in common diagnostic procedures such as mammography. However, very few statistical approaches are available to assess agreement in these large-scale settings. Many existing summary measures of agreement rely on extensions of Cohen's kappa. These are prone to prevalence and marginal distribution issues, become increasingly complex for more than three experts, or are not easily implemented. Here we propose a model-based approach to assess agreement in large-scale studies based upon a framework of ordinal generalized linear mixed models. A summary measure of agreement is proposed for multiple experts assessing the same sample of patients' test results according to an ordered categorical scale. This measure avoids some of the key flaws associated with Cohen's kappa and its extensions. Simulation studies are conducted to demonstrate the validity of the approach with comparison with commonly used agreement measures. The proposed methods are easily implemented using the software package R and are applied to two large-scale cancer agreement studies.
| S-EPMC4560692 | biostudies-literature
Project description:In clinical studies, it is often of interest to see the diagnostic agreement among clinicians on certain symptoms. Previous work has focused on the agreement between two clinicians under two different conditions or the agreement among multiple clinicians under one condition. Few have discussed the agreement study with a design where multiple clinicians examine the same group of patients under two different conditions. In this paper, we use the intraclass kappa statistic for assessing nominal scale agreement with such a design. We derive an explicit variance formula for the difference of correlated kappa statistics and conduct hypothesis testing for the equality of kappa statistics. Simulation studies show that the method performs well with realistic sample sizes and may be superior to a method that did not take into account the measurement dependence structure. The practical utility of the method is illustrated on data from an eosinophilic esophagitis (EoE) study.
| S-EPMC9631095 | biostudies-literature
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.
| S-EPMC7261640 | biostudies-literature