The statistical analysis software IBM SPSS (Statistical Package for the Social Sciences), v24 was used to produce descriptive statistics. Associations between retinal sensitivity and lesions of RVO were investigated using the R software environment (The R Foundation for Statistical Computing, Vienna, Austria, version 3.4.3). Mixed-effects linear regression models were fitted to estimate associations between retinal sensitivity (function) and RVO lesions (structure), including the presence/absence of retinal ischemia, retinal hemorrhage, IRF, SRF, and the integrity of the ELM and IS/OS layer. All analyses were of complete cases (at the point level); points where imaging measurements were taken but ungradable points were included. A random effect term was used to represent the variation in retinal sensitivity among included patients and fixed effects were used for structural parameters. This model included all baseline measurements for both affected and fellow eyes (a maximum of 162 points for each patient, 81 points from each eye). Lesion status was determined at the point level (rather than at the eye level). Fellow eyes contributed points with no lesions; affected eyes contributed with a mixture of points, some with lesions, some without them, with the proportion of points affected varying among lesion types (i.e., each point in an RVO eye would be classified as affected or unaffected and, if affected, whether there was hemorrhage, ischemia, SRF, IRF, etc., on it). Points within the same patient were not treated as independent observations; correlations within eyes were expected because points were close together; correlations between eyes were also expected because each patient was likely to have a given aptitude for the microperimetry testing. These dependencies were modelled using a single patient-level random effect that allowed patients to differ in their retinal sensitivity. Simultaneously, the effect of interest—the average difference in sensitivity across all eyes between points with lesions and those without lesions—was estimated in the fixed effects part of the model. Therefore, the reported coefficients denote the difference in sensitivity between points with lesions and those without lesions, accounting for differences among patients in test aptitude and for the different retinal regions sampled, with a large number of normal points provided by fellow eyes.
To quantify the relationship between retinal sensitivity and total retinal thickness as well as the thickness of the GCL-IPL, separate regressions were fitted for the subset of points in which these measurements were taken. A regression was fitted for each variable, added as a fixed effect to the previous model of retinal sensitivity at baseline. Before undertaking model fitting, thickness measures were standardized by subtracting the mean and dividing by the standard deviation (µm). Statistical significance was considered when the P was less than 0.05.
A separate analysis was conducted to evaluate the relationships between the change in retinal sensitivity from baseline to 6 months and the various RVO lesions. Only affected eyes were included. Comparisons were made at the pointwise level explicitly comparing sensitivity at the exact same location in the affected eye at baseline and the 6-month follow-up. The outcome variable (sensitivity difference) was calculated as: Affected eye at point x at baseline – affected eye at point x at the 6-month follow-up. A positive value would indicate improved sensitivity, whereas a negative value would indicate a deterioration. Statistical significance was considered when the P value was less than 0.05.