Data were summarized using mean and standard deviation (SD) for approximately normally distributed variables and median and interquartile range (IQR) for all other variables. First and fifth percentiles of RNFL thickness were calculated assuming a Gaussian distribution. Where data were available, both eyes of individuals were included in this analysis. Descriptive data on RNFL thickness are presented for each eye separately. To analyze associations with ocular parameters, including RNFL thickness, we used linear mixed models with a random intercept term included for each participant to account for the correlation between eyes. Models were specified as follows: yi = β0 + β1x1 + ui, where y is the outcome (RNFL thickness), β are the coefficients, x1 is the variable of interest (more variable and coefficient terms were added for multivariable models), and ui is the random intercept term that varies for each participant (i). Age, sex, ethnicity, smoking status, axial length, and IOP were identified a priori as variables potentially associated with RNFL. The P value for significance was set at 0.05, but a Bonferroni correction was applied when investigating associations between demographic and ocular parameters and RNFL thickness in each of the seven RNFL sectors, separately, to account for multiple testing (Bonferroni-corrected P value threshold = 0.007). Heteroscedasticity was assessed from residual versus fitted plots. When investigating the frequency of WNL, BL, and ONL RNFL thickness classification, the prevalence and confidence intervals (CIs) for each of the WNL, BL, and ONL classifications were calculated for each RNFL sector: global, nasal, superonasal, inferonasal, temporal, superotemporal, or inferotemporal. To account for multiple testing (seven tests), a Bonferroni correction (1 – (0.05/7)) was applied and 99.3% CIs were adopted.
Frequency tables and weighted κ statistics (evenly spaced weights) were calculated to compare the agreement between the manufacturer database RNFL thickness classification and RNFL thickness classification developed based on first and fifth centiles of RNFL thickness in this study and RNFL thickness classifications in the right versus the left eye.
Analyses were conducted in R version 4.0.3 (R Foundation for Statistical Computing, Vienna, Austria).