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A Bland-Altman diagram (differential diagram) in analytical chemistry or biomedicine is a data representation method used to analyze the correspondence between two different assays. It is identical to a Tukey mean difference table,[1] the name by which it is known in other fields, but was popularized in medical statistics by J. Martin Bland and Douglas G. Altman. [2] [3] One of the most important ways to classify different methods is to divide them into those that produce standardized conformity indices that are scaled to be within a certain range (e.B. the CCC is scaled between −1 and 1 and the CIA between 0 and 1), and those that allow direct comparison with the original scale of the data and require the specification of a clinical difference acceptable (e.B LoA, CP and TDI methods). These groups of methods are commonly referred to as scaled or unscaled tuning methods [2], and these latter sets of methods are sometimes referred to as « pure chord indicators » [40]. In fact, the CCC can be more accurately described as an evaluation of distinction and not as a correspondence, since it is designed to calculate the proportion of variance of a system explained by the subject/activity effect and does not require the specification of a DAC [41]. It is therefore not a « pure correspondence index » [41]. The CCC has the disadvantage of being highly dependent on variability between subjects (and in our case also on variability between subjects) and would therefore reach a high value for a population with significant heterogeneity between subjects or activities, although agreement within subjects may be low [2, 11, 12]. If the gaps between subjects and between activities are very small, the CCC is unlikely to reach a high value, even if agreement on devices is appropriate. We suggest that researchers consider using the coverage probability method alongside a graphical representation of raw data in method comparison studies.

In case of disagreement between devices, it is important to look beyond the general summary correspondence clues and consider the underlying causes. The graphical summary of the data and the study of the model parameters can help with this. Zou GY. Estimation of the confidence interval for Bland-Altman chord limits with several observations per individual. Stat Methods Med Res. 2013;22:630. where ( {mu}_0^{ast } ) the bias of the middle is interesting. The limits of the match are then calculated as Roy A. An application of the linear mixed-effects model to assess the agreement between two methods with replicated observations.

J Biopharm Stat. 2009;19(1):150–73. Hamilton C, Stamey JD Using a predictive approach to assess the agreement between two continuous measures. J Clin Monit Comput. 2009;23(5):311–4. If the assumptions described above are not valid, nonparametric methods should be considered. For example, Perez-Jaume and Carrasco propose a non-parametric alternative to the calculation of the TDI that is more stable and reliable than the parametric method when working with distorted data [30]. It is also relatively easy to calculate and less affected by outliers or extremes than the parametric approach. The method is simply to calculate the quantiles of an ordered list of matched differences to calculate the TDI. A bootstrap method can then be used to calculate the upper limit by resampling at the patient level and then recalculating the TDI for each new bootstrap sampling.

This seems to be the same as a percentile method first described by Bland and Altman [5], except that in the case of repeated measurements, we use bootstrap resampling to get the upper limit. Although it does not assume a normal distribution, we must always assume that the paired differences are independent and distributed identically. Other non-parametric methods are available [31, 32]. Stevens [33] also developed a generalization of the probability of agreement based on the moment method, which does not require a distribution assumption for real values. Entirely Bayesian versions of the tuning limit method have also been proposed, for example Schluter`s Bayesian chord method [34]. In addition, Barnhart [12] and Barnhart et al. [11] describe an interesting method that uses generalized estimation equations to obtain a nonparametric estimate of CP. Recently, Jang et al. [35] proposed a new set of correspondence indices adapted to contexts where there are multiple assessors and heterogeneous variances.

Hamilton C, Stamey J. Using Bland-Altman to assess the match between two medical devices – Don`t forget about confidence intervals! J Clin Monit Comput. 2007;21(6):331–3. Confidence intervals for mean difference and agreement limits indicate uncertainty in the estimates. The large intervals are due to the small sample size and the large variation in differences. Even the most optimistic interpretation would conclude that the agreement is unacceptable. where yijlt is the reading/measurement of the respiratory rate performed on subject I with device j when performing activity l at time t; μ is the total average; ( {alpha}_isim Nleft(0,{sigma}_{alpha}^2right) ) is the random subject effect; βj is the solid effect of the device we need for reasons of identifiability β1 + β2 = 0; ( {gamma}_lsim Nleft(0,{sigma}_{gamma}^2right) ) is the random activity effect, and ( {varepsilon}_{ijlt}sim Nleft(0,{sigma}_{varepsilon}^2right) ) is the residual error. We extend and modify this basic model for each of the specific agreement methods listed below. .