Agreement Between A Measured Value

I thought this meeting would be a good opportunity to mark this surprising event, so I`ll tell you a little bit about how we came to write, tell you something about what`s happening now, and show you a recent example that illustrates several points about statistics and medicine. Deceptive measures of matching The paper was a fantastic success, beyond my most daring dreams. In this paper, we focused on the details of the limitations of the agreement approach and used that term for the first time. According to our original author plan, the second document was published under the names Bland and Altman, and the method is therefore known as the “Bland Altman Method.” Sorry, Doug. Measurement is an important part of clinical medicine and the development and evaluation of new measurement methods is an important research activity. Misinterpretation of simple statistics is often used to analyze such studies. As a result, the limits of the agreement`s approach, which has been widely adopted, have been developed. Many problems persist and I will illustrate some of them with a recent example of blood glucose measurement. This is clearly less than one, and it depends only on the relative sizes of sT2, sA2 and sB2.

If sA2 and sB2 are not small compared to sT2, the correlation is low, regardless of how good the agreement between the two methods is. The simple 95% limits of the agreement method are based on the assumption that the average value and standard deviation of differences are constant, i.e. they do not depend on the size of the measurement. In our original documents, we described the frequent situation where the standard deviation is proportional to size, and described a method that uses a logarithmic transformation of the data. In our 1999 review paper (Bland and Altman 1999), we described a method to avoid any relationship between the average and the SD of the differences and magnitude of the measurement. (It was Doug Altman`s idea, I can`t take recognition.) As mentioned above, the more measures there are, the closer we can get to the knowledge of the actual value of a quantity. For several measurements (replications), we can evaluate the accuracy of the results, and then use simple statistics to estimate how close the average value would be to the actual value if there was no systematic error in the system. The average value deviates less from the “real value” as the number of measures increases. Accuracy is the proximity of a measure to fair value for this measurement. The accuracy of a measurement system refers to the proximity of the concordance between repeated measurements (repeated under the same conditions). Measurements can be both accurate and precise, accurate, but not precise, accurate, but not accurate, but not accurate or not. I think statisticians have significantly improved the quality of medical research.

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