Carkeet A, Goh YT. Confidence and coverage for Bland-Altman limits the agreement and their approximate confidence intervals. Med Res Stat Methods. 2018;27:1559-74. Carkeet A. Exact parametric confidence intervals for Bland-Altman compliance limits. Optom Vis Sci. 2015;92:e71-80. On the other hand, in establishing confidence intervals between the boundaries of agreements or percentiles, Bland and Altman [2] argued that var[S] ≐ 2/(2) and Var [2) and Var [2) B] ≐ b-2/N, the B-1-for example _p. As they got closer, they proposed the simplified limit key amount of the agreement estimate of the interval in which some of the differences lie between the measures.

Bland JM, DG Altman. The measurement agreement in method comparison studies. Med Res Stat Methods. 1999; 8:135-60. Myles – Cui. Use of the Bland-Altman method to measure compliance with repeated measurements. BJA: British Journal of Anaesthesia, Volume 99, issue 3, 1 September 2007, pages 309-311, doi.org/10.1093/bja/aem214. The academic.oup.com/bja/article/99/3/309/355972 on April 23, 2018 are widely used as benchmarks for determining the relative size and essential importance of quantitative measurements. The Bland-Altman limits advocated by the agreement are an important application.

In addition to the above studies, chakraborti and Li presented a numerical comparison of several methods of estimating the interval of normal percentiles [24]. They adopted a standardized minimum and unbiased estimate as a precise regime quantity and proposed accurate and approximate confidence intervals of ordinary percentiles. Their simulation study showed that the expected width and probability of coverage of the exact and approximate methods proposed are almost identical to those described in Lawless ([25], p. 231). Despite the analytical arguments and empirical results in Chakraborti and Li [24], the following two attentions should be recalled to their illustration. First, while Lawless` confidence intervals [25] have been shown to be identical to the existing formulas in Owen [15] and Odeh and Owen [18], they have not discussed the theoretical implications between their precise method and the precise established procedure. Second, unlike the asymmetry of exact confidence intervals, the approximate confidence intervals of Chakraborti and Li [24] are equal to an unbiased estimate of the minimum variance. Note that both ends of a bilateral confidence interval can also be interpreted as the limits of the unilateral confidence interval. It is therefore appropriate to continue to evaluate the performance of the two limits of the approximate interval method of Chakraborti and Lis [24] with respect to the ownership of egaltailed. The analytical and numerical results in Chakraborti and Li [24] are not detailed enough to address these fundamental issues. It is wise to shed light on these essential aspects of their methods, which are accepted as a viable technique.

Compliance limitations include both systematic errors (bias) and random errors (precision) and provide a useful measure for comparing likely differences between different results measured using two methods. If one method is a reference method, compliance limits can be used as a measure of the total error of a measurement method (Krouwer, 2002). In particular, 10,000 iteration simulation studies were conducted to calculate the probability of simulated coverage of accurate and approximate confidence intervals for percentiles in a standard distribution N (0, 1).