Corneal pachymetry in normal and keratoconic eyes:Orbscan II versus ultrasound

Purpose: To compare corneal thickness measurements using Orbscan II (OII) and ultrasonic (US) pachymetry in normal and in keratoconic eyes. Setting: Eye Department, Heartlands and Solihull NHS Trust, Birmingham, United Kingdom. Methods: Central corneal thickness (CCT) was measured by means of OII and US pachymetry in 1 eye of 72 normal subjects and 36 keratoconus patients. The apical corneal thickness (ACT) in keratoconus patients was also evaluated using each method. The mean of the difference, standard deviation (SD), and 95% limits of agreement (LoA = mean ± 2 SD), with and without applying the default linear correction factor (LCF), were determined for each sample. The Student t test was used to identify significant differences between methods, and the correlation between methods was determined using the Pearson bivariate correlation. Bland-Altman analysis was performed to confirm that the results of the 2 instruments were clinically comparable. Results: In normal eyes, the mean difference (± 95% LoA) in CCT was 1.04 μm ± 68.52 (SD) (P>.05; r = 0.71) when the LCF was used and 46.73 ± 75.40 μm (P = .0001; r = 0.71) without the LCF. In keratoconus patients, the mean difference (± 95% LoA) in CCT between methods was 42.46 ± 66.56 μm (P<.0001: r = 0.85) with the LCF, and 2.51 ± 73.00 μm (P>.05: r = 0.85) without the LCF. The mean difference (± 95% LoA) in ACT for this group was 49.24 ± 60.88 μm (P<.0001: r = 0.89) with the LCF and 12.71 ± 68.14 μm (P = .0077; r = 0.89) when the LCF was not used. Conclusions: This study suggests that OII and US pachymetry provide similar readings for CCT in normal subjects when an LCF is used. In keratoconus patients, OII provides a valid clinical tool for the noninvasive assessment of CCT when the LCF is not applied. © 2004 ASCRS and ESCRS.

Statnote 14: the correlation of two variables (Pearson's 'r')

Pearson's correlation coefficient (‘r’) is one of the most widely used of all statistics. Nevertheless, care needs to be used in interpreting the results because with large numbers of observations, quite small values of ‘r’ become significant and the X variable may only account for a small proportion of the variance in Y. Hence, ‘r squared’ should always be calculated and included in a discussion of the significance of ‘r’. The use of ‘r’ also assumes that the data follow a bivariate normal distribution (see Statnote 17) and this assumption should be examined prior to the study. If the data do not conform to such a distribution, the use of a non-parametric correlation coefficient should be considered. A significant correlation should not be interpreted as indicating ‘causation’ especially in observational studies, in which the two variables may be correlated because of their mutual correlations with other confounding variables.

Data analysis methods in optometry. Part 5: correlation

1. Pearson's correlation coefficient only tests whether the data fit a linear model. With large numbers of observations, quite small values of r become significant and the X variable may only account for a minute proportion of the variance in Y. Hence, the value of r squared should always be calculated and included in a discussion of the significance of r. 2. The use of r assumes that a bivariate normal distribution is present and this assumption should be examined prior to the study. If Pearson's r is not appropriate, then a non-parametric correlation coefficient such as Spearman's rs may be used. 3. A significant correlation should not be interpreted as indicating causation especially in observational studies in which there is a high probability that the two variables are correlated because of their mutual correlations with other variables. 4. In studies of measurement error, there are problems in using r as a test of reliability and the ‘intra-class correlation coefficient’ should be used as an alternative. A correlation test provides only limited information as to the relationship between two variables. Fitting a regression line to the data using the method known as ‘least square’ provides much more information and the methods of regression and their application in optometry will be discussed in the next article.