Normality is a common assumption for many quality control CHARTs. One should expect misleading results once this assumption is violated. In order to avoid this pitfall, we need to evaluate this assumption prior to the use of control CHARTs which require normality assumption. However, in certain cases either this assumption is overlooked or it is hard to check. Robust control CHARTs and bootstrap control CHARTs are two remedial measures that we could use to overcome this issue. In this paper, a new bootstrap algorithm is proposed to construct Hotelling’ s T2 control CHART. The performance of proposed CHART is evaluated through a simulation study. Our results are compared to the traditional Hotelling’ s T2 control CHART results and the bootstrap results reported by Phaladiganon et al. [13] using in-control and out-of-control average run lengths denoted by ARL0 and ARL1, respectively. The latter case is obtained when the process mean is subject to sustained shifts. Numerical results indicate that the proposed algorithm performs better than the above mentioned methods. The new bootstrap algorithm is also applied to a real data set.

Measurement of Visual Acuity (VA) has been used for eye examinations under various clinical conditions. In addition, because of changes in VA with a range of eye diseases, its measurement is used to screen, diagnose, and evaluate response to treatment in numerous eye diseases and predict the patient's visual function. VA and the likelihood of astigmatism could be evaluated simultaneously using a newly designed Stripped Circle (SC) VA CHART. This CHART has been developed based on the standard VA measurement scale CHART, and the Landolt-C (LC) VA CHART and all the standards for a VA CHART design were implemented during its development. However, to publicize the clinical application of this VA measurement CHART, further studies are required to evaluate its sensitivity, specificity, and repeatability compared with a similar standard VA CHART.

Background: Taking visual acuity is an important part of an eye routine examination. This study was conducted to compare visual acuity measured using an Iranian digital tumbling E CHART with visual acuity measured using an Italian digital tumbling E CHART as a familiar foreign CHART.Materials and Methods: This study was carried out on 200 participants (400 eyes; healthy n=40, glaucoma n=40, retinal disorders n=40, post lasik n=40, corneal n=40) with mean age 44.28 years. Visual acuity of both eyes in half of the subjects (n=100, n=20 from each group) was first measured using the Italian digital CHART and then measured using Iranian digital CHART. Visual acuity of the rest of subjects (n=100, n=200 from each group) was first measured using the Iranian digital CHART and then measured using the Italian digital CHART. Measured visual acuities were recorded in logMAR notation.Results: Mean of the Iranian and Italian Tumbling E CHART is 0.280 ± 0.012 and 0.277±0.012, respectively. Paired t-test used to evaluate the mean difference between two groups (p=0.721) indicated that there was no statistically significant difference between means of the two tests. Spearman correlation coefficient for the results of both tests was 0.942 (p<0.001), which was statistically significant and could be categorized as a strong positive correlation.Conclusion: The two digital Tumbling E CHARTs acted similarly at different levels of acuity in different disorders. However, the Iranian CHART requires a more accurate design for optotypes of the lower acuity lines in order to obtain more accurate measurement of visual acuities in healthy subjects.

Multivariate control CHARTs such as Hotelling's T2 and X2 are commonly used for monitoring several related quality characteristics. These control CHARTs use correlation structure that exists between quality characteristics in an attempt to improve monitoring. The purpose of this article is to discuss some issues related to the G CHART proposed by Levinson et al. [9] for detecting shifts in the process variance-covariance matrix. They use a G statistic which is distributed as a chi-square with p(p+1)/2 degrees of freedom where p denotes the number of variables under study. The authors show through simulation that the chi-square distribution only holds for certain cases. The results could be important to practitioners who use G CHART for monitoring purposes.