Longitudinal analysis of ERG data.
REGRESSION
Data processing of ERG parameters was performed as closely as possible to the original study in consultation with the original data manager of the study (C Weigel DiFranco) (Over the course of several iterations of data processing (e.g., as more participants were solved genetically and added), it was noted that when looking at small subsets of participants, there were unstable estimates of the effects of vitamin A and E on progression rates, with small changes in the input data set (data not shown). Power calculations further supported the notion that very small subgroups should not be analyzed. Therefore, analyses for single-gene subgroups were only performed for the larger gene groups: There are some participants with additional long-term follow-up data that are now available, including those who participated in multiple trials and accumulated very long total follow-up time. Initial regression modeling using imbalanced, very-long-term data (e.g., 23 years) from some participants and only year 4–6 data from the rest of the participants produced results that were overly weighted by the outcomes of the very-long-term participants (data not shown). It also would have required additional variables to model participants who switched vitamin A treatment status during these longer follow-up time periods. Therefore, for each participant, only the first 6 years of data were used for regression models.Subsequent analyses were performed in R; see 