1. Totally wrong analysis: this is the most common scenario. Gupta PP and Dipti Agarwal compared peak exploratory flow to two methods [7]. They reported comparison with the t-test and the Pearson correlation coefficient. Both the authors and editors of the journal missed this monstrous analysis. A Bland Altman diagram (difference diagram) in analytical chemistry or biomedicine is a data trembling method used to analyze the concordance between two different as trials. It is identical to a mean tukey difference graph,[1] the name by which it is known in other fields, but was popularized in the medical statistics of J. Martin Bland and Douglas G. Altman.

[2] [3] It is important that there are no uniform criteria for what constitute acceptable limits of compliance. This is a subjective decision that must be made from a clinical point of view and must depend on and pre-specify the variable to be measured. «The CIA has since paid more than a million dollars in accordance with the agreement,» the report said. 2. Mention of the Bland Altman method without using it: the Bland Altman method is probably the most used if it is not used at all. Jaramillo et al. compared MR imaging to conventional arthrography [8]. The Bland Altman method was cited in the method section, but the results section reports correlations and rank correlations with p-values, not mentioning 95% of confidence limits that are an integral part of the Bland Altman method.

This is unacceptable if the researcher is aware of the right analysis strategy. A common question in clinical research is whether a new measurement method is equivalent to an established method. As a statistical advisor at PHASTAR, I see an increase in the number of studies that compare a new artificial intelligence or machine learning diagnostic tool to either an existing tool or a clinician. The methodology for analyzing binary data is well established, but the methodology for continuous results is less developed. Here, we`ll review the current methodology and sketch out some of the most common pitfalls. It should be noted that concordance analysis does not guarantee the accuracy of the measurement methods, but shows to what extent different measurement techniques correspond. In order to properly evaluate a new measurement method, it is also necessary to take into account the dimensions relating to the validity of measurements such as sensitivity, specificity and positive and negative forecasts. For the same reasons, other methods of evaluating associations such as t-test/analysis of variance (ANOVA) are also not appropriate for analyzing compliance studies. Duran R et al. used ANOVA to compare 3 methods to measure the temperature of low birth weight preterm infants [4]. He concluded a good concordance, as there was no statistically significant difference between the mean temperatures of the middle forehead and the temperature of the armpit. By observing the binary results, the diagonal elements of a contingency table show the frequency of the concordance.

Cohens Kappa is a measure of compliance that is considered more robust than a simple percentage agreement, since it takes into account the possibility that the agreement will occur randomly. It is given by: Mill`s Methods are five methods of induction described by the philosopher John Stuart Mill in his book A System of Logic published in 1843. [1] That they shed light on questions of causality. A paradigm shift in health management, from hospital care to home care, which began in industrialized countries, will eventually find its way to developing countries with some delay. .