# Proportional Error Bland Altman

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## Bland Altman Interpretation

graphical objects Reference lines F7 - Repeat key Notes editor bland altman plot . excel File menu New Open Save Save as Add file Export Page setup Print Properties Exit Edit bland altman plot spss menu Undo Cut Copy Paste Delete Select all Find Find & replace Go to cell Fill Insert - Remove Transpose View menu Spreadsheet Show formulas## Bland Altman Plot R

Show gridlines Contents bar Toolbars Status bar Full screen Format menu Font Increase font size Decrease font size Spreadsheet Format graph Graph legend Reset graph titles and options Tools menu Sort rows Exclude & Include Fill column Stack columns Generate random sample Create groups Create groups form quantiles Create random groups Create## Reporting Bland Altman Results

user-defined groups Rank cases Percentile ranks z-scores Power transformation Edit variables list Edit filters list Select variable for case identification Enter key moves cell pointer Options Statistics menu Summary statistics Outlier detection Distribution plots Histogram Cumulative frequency distribution Normal plot Dot plot Box-and-whisker plot Correlation Correlation coefficient Partial correlation Rank correlation Scatter diagram Regression Regression Scatter diagram & regression line Multiple regression Logistic regression Probit regression (Dose-Response analysis) Nonlinear regression T-tests One sample t-test Independent samples t-test Paired samples t-test Rank sum tests Signed rank sum test (one sample) Mann-Whitney test (independent samples) Wilcoxon test (paired samples) Variance ratio test (F-test) ANOVA One-way analysis of variance Two-way analysis of variance Analysis of covariance Repeated measures analysis of variance Kruskal-Wallis test Friedman test Crosstabs Chi-squared test Fisher's exact test McNemar test Cochran's Q test Relative risk & Odds ratio Frequencies bar charts Survival analysis Kaplan-Meier survival analysis Cox proportional-hazards regression Meta-analysis IntroductionTechnology, Auckland 1020, New Zealand. Email. Reviewer: Alan M Batterham, Sport and Exercise Science, University of Bath, Bath BA2 7AY, UK. An instrument that has been calibrated against a criterion measure with a sample of subjects understanding bland altman analysis is sometimes checked against the criterion in a validity study with another sample.

## Bland Altman Plot Example

In a spreadsheet-based simulation of such calibration and validity studies, a Bland-Altman plot of difference vs mean values for bland altman plot matlab the instrument and criterion shows a systematic proportional bias in the instrument's readings, even though none is present. This artifactual bias arises in a Bland-Altman plot of any measures with substantial random https://www.medcalc.org/manual/blandaltman.php error. In contrast, a regression analysis of the criterion vs the instrument shows no bias. The regression analysis also provides complete statistics for recalibrating the instrument, if bias develops or if random error changes since the last calibration. The Bland-Altman analysis of validity should therefore be abandoned in favor of regression. KEYWORDS: calibration, method comparison, random error, systematic error, standard error of the estimate. http://www.sportsci.org/jour/04/wghbias.htm Reprintpdf· Reprintdoc·Spreadsheet·Reviewer's Commentary For comparison of one method with another, Bland and Altman (1986) advised researchers to use the two methods on a group of subjects, then plot the difference scores against the mean for each subject. Such plots have become a standard accessory in validity or method-comparison studies, and their original paper has been cited over 9000 times. In this article I use a spreadsheet to show that the plots can lead to an incorrect conclusion about the validity of a measure, and I urge researchers to use regression when comparing measures. Bland and Altman devised their plot to steer researchers away from what they considered was misuse of the correlation coefficient as a measure of validity. The misuse amounted to regarding the correlation coefficient as the most important or even the only measure of the relationship between two measures. The problem with the correlation coefficient is that the two measures might be highly correlated, yet there could be substantial differences in the two measures across their range of values. An appropriate comparison of the two measures needs to highlight such differences—hence the Bland-Altman plot, which explicitly shows diff& Reprints Resources Clinical Chemistry Trainee Council Clinical Case Studies Clinical Chemistry Guide to Scientific Writing Clinical Chemistry Guide to Manuscript Review Journal Club Podcasts Q&A Translated Content Abstracts Submit Contact Other http://clinchem.aaccjnls.org/content/48/5/799 PublicationsThe Journal of Applied Laboratory Medicine User menu Subscribe My alerts Log in http://www-01.ibm.com/support/docview.wss?uid=swg21476730 Search Search for this keyword Advanced search Other PublicationsThe Journal of Applied Laboratory Medicine Subscribe My alerts Log in Search for this keyword Advanced Search Home AboutClinical Chemistry Editorial Board Most Read Most Cited Alerts ArticlesCurrent Issue Early Release Future Table of Contents Archive Browse by Subject Info forAuthors Reviewers Subscribers bland altman Advertisers Permissions & Reprints Resources Clinical Chemistry Trainee Council Clinical Case Studies Clinical Chemistry Guide to Scientific Writing Clinical Chemistry Guide to Manuscript Review Journal Club Podcasts Q&A Translated Content Abstracts Submit Contact LetterLetters Application of the Blandâ€“Altman Plot for Interpretation of Method-Comparison Studies: A Critical Investigation of Its Practice Katy Dewitte, Colette Fierens, Dietmar StĂ¶ckl, Linda M. Thienpont Published May 2002 Katy DewitteFind this bland altman plot author on Google ScholarFind this author on PubMedSearch for this author on this siteColette FierensFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteDietmar StĂ¶cklFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteLinda M. ThienpontFind this author on Google ScholarFind this author on PubMedSearch for this author on this site ArticleFigures & DataInfo & Metrics PDF To the Editor: Current guidelines for the combined graphical/statistical interpretation of method-comparison studies (1) include a scatter plot combined with correlation and regression analysis (2) and/or a difference plot combined with calculation of the 2s limits of the differences between the methods (the so-called 95% limits of agreement) (3)(4). The former approach has a long tradition in clinical chemistry, and its advantages and pitfalls are well known (5). The latter approach, however, which was deemed â€śsimple both to do and to interpretâ€ť and was propagated as a substitute for regression analysis (4)(5), became available only in recent years and has increased in popularity. The general features of the Blandâ€“Altman plot have been well described (4) (see also Fig. 1Aâ‡“ ). The x axis show

is a Bland-Altman plot, and can one be produced in SPSS? Resolving the problem The Bland-Altman plot (Bland & Altman, 1986) is most likely to be seen in the medical statistics literature. Suppose there are two techniques for measuring some continuously-scaled variable, each having some error, and we want a graphical means to assess whether or not they are comparable. Say one wanted to compare two techniques of measuring some blood factor. Data for the plot would be collected by gathering a number of blood samples, splitting each in two, and measuring the factor using both methods. The Bland-Altman chart is a scatterplot with the difference of the two measurements for each sample on the vertical axis and the average of the two measurements on the horizontal axis. Three horizontal reference lines are superimposed on the scatterplot - one line at the average difference between the measurements, along with lines to mark the upper and lower control limits of plus and minus 1.96*sigma, respectively, where sigma is the standard deviation of the measurement differences. (Bland and Altman also discuss the option of using confidence interval bounds, based on the standard error of the mean, for the upper and lower reference lines.) If the two methods are comparable, then differences should be small, with the mean of the differences close to 0, and show no systematic variation with the mean of the two measurements. 'Small' would be an amount that would be clinically insignificant for the factor being measured. The reference is: Bland, J.M., & Altman, D.G. (1986). Statistical methods for assessing agreement between two methods of clinical measurement. Lancet, 327 (8476), 307-310. While SPSS does not have facilities specifically for producing Bland-Altman charts, they can be produced in SPSS, with help from the