Investigative Ophthalmology & Visual Science: Low Vision Topic Collection
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en-usTue, 21 Jul 2020 16:03:34 GMTSun, 27 Sep 2020 21:43:48 GMTSilverchaireditor@iovs.arvojournals.orgwebmaster@iovs.arvojournals.orgAqueous Flare and Progression of Visual Field Loss in Patients With Retinitis Pigmentosa
https://iovs.arvojournals.org/article.aspx?articleid=2770343
Tue, 21 Jul 2020 16:03:34 GMTFujiwara K, Ikeda Y, Murakami Y, et al. <span class="paragraphSection"> <div class="boxTitle">Purpose</div> To investigate the association between aqueous flare and progression of visual field loss using the Humphrey Field Analyzer in patients with retinitis pigmentosa (RP). <div class="boxTitle">Methods</div> We examined a total of 101 eyes of 101 patients who were diagnosed with typical RP. Sixty-one percent of the patients were female, and the mean age of the total group was 47.4 years. Aqueous flare, visual field (by an Humphrey Field Analyzer, the central 10-2 SITA-Standard program), and optical coherence tomography measurements were obtained for all patients. The slope, which was derived from serial values of mean deviation, macular sensitivity, or foveal sensitivity for each eye with univariate linear regression, was used for analysis. <div class="boxTitle">Results</div> Aqueous flare values were significantly correlated with the mean deviation slope (r = −0.20, <span style="font-style:italic;">P</span> = 0.046), macular sensitivity slope (r = −0.28, <span style="font-style:italic;">P</span> = 0.005) and foveal sensitivity slope (r = −0.20, <span style="font-style:italic;">P</span> = 0.047). The values of the retinal sensitivity slope significantly decreased as the aqueous flare level increased (all <span style="font-style:italic;">P</span> < 0.05). These associations remained unchanged after adjustment for age, sex, and posterior subcapsular cataract, and epiretinal membrane. <div class="boxTitle">Conclusions</div> Elevation of aqueous flare is a risk factor for the decline of central visual function in RP. Aqueous flare may be a useful marker for disease progression in RP. </span>https://iovs.arvojournals.org/article.aspx?articleid=2770343Parametric Statistical Inference for Comparing Means and Variances
https://iovs.arvojournals.org/article.aspx?articleid=2770337
Tue, 21 Jul 2020 16:03:34 GMTLedolter J, Gramlich OW, Kardon RH. <span class="paragraphSection"> <div class="boxTitle">Purpose</div> The purpose of this tutorial is to provide visual scientists with various approaches for comparing two or more groups of data using parametric statistical tests, which require that the distribution of data within each group is normal (Gaussian). Non-parametric tests are used for inference when the sample data are not normally distributed or the sample is too small to assess its true distribution. <div class="boxTitle">Methods</div> Methods are reviewed using retinal thickness, as measured by optical coherence tomography (OCT), as an example for comparing two or more group means. The following parametric statistical approaches are presented for different situations: two-sample t-test, Analysis of Variance (ANOVA), paired t-test, and the analysis of repeated measures data using a linear mixed-effects model approach. <div class="boxTitle">Results</div> Analyzing differences between means using various approaches is demonstrated, and follow-up procedures to analyze pairwise differences between means when there are more than two comparison groups are discussed. The assumption of equal variance between groups and methods to test for equal variances are examined. Examples of repeated measures analysis for right and left eyes on subjects, across spatial segments within the same eye (e.g. quadrants of each retina), and over time are given. <div class="boxTitle">Conclusions</div> This tutorial outlines parametric inference tests for comparing means of two or more groups and discusses how to interpret the output from statistical software packages. Critical assumptions made by the tests and ways of checking these assumptions are discussed. Efficient study designs increase the likelihood of detecting differences between groups if such differences exist. Situations commonly encountered by vision scientists involve repeated measures from the same subject over time, measurements on both right and left eyes from the same subject, and measurements from different locations within the same eye. Repeated measurements are usually correlated, and the statistical analysis needs to account for the correlation. Doing this the right way helps to ensure rigor so that the results can be repeated and validated. </span>https://iovs.arvojournals.org/article.aspx?articleid=2770337