2.2. Analysis
Contrast sensitivity data were submitted to a Type III sums-of-squares 2 (spatial frequency) x 2 (subject group) mixed-model analysis of variance (ANOVA); this was done once with the raw contrast sensitivity data and once again with the log-transformed data. In each case, a Greenhouse-Geisser correction was applied to account for violation of the sphericity assumption (Mauchly’s test). In all cases, follow-up t-tests assumed equal variances unless a significant Levene’s test required otherwise. Note that we simplified the analyses by examining only the two most extreme spatial frequency conditions–0.5 and 21 cycles per degree–since the former has been claimed to most clearly bias processing toward the magnocellular stream and the latter toward the parvocellular stream and since group interaction effects should be clearly observed at these endpoints (Butler et al., 2005). Note also that we confined our results only to the shortest stimulus duration (33 ms) since these stimulus types have been used in previous schizophrenia studies that sought to bias processing toward the magnocellular channel (Butler et al., 2005; Martinez et al., 2012; Revheim et al., 2014). Nevertheless, we also (and separately) analyzed the 500 ms stimulus duration data for completeness.
To corroborate the log-transformed results, we performed generalized estimating equations (GEE) analysis using the raw (non-transformed) contrast sensitivity data. The analysis incorporated a gamma distribution function with a log link (to reflect the relationship of mean and variance in contrast sensitivity data), robust estimators of the standard errors, a maximum likelihood estimation of the parameters, and an exchangeable correlation structure (since conditions were counterbalanced). GEE’s advantage over generalized linear mixed models (GLMMs) is that it can handle heteroscedasticity, does not require correct specification of the covariance structure, makes fewer assumptions, and is more robust when only population-level (marginal) effects are desired (Pekár & Brabec, 2018).
To consider the role of visual acuity, we also conducted analyses that matched groups on acuity (Elliot, 2016) and included this variable as a covariate. We opted to remove high-acuity controls rather than lower-acuity patients since i) poor acuity may characterize the illness (Hayes et al., 2018; Shoham et al., 2021), ii) most healthy people with poor acuity never develop psychosis; and iii) it is inappropriate to add an illness-related covariate when the groups are not matched on the covariate (Miller & Chapman, 2001). Potential issues with interpretability of visual acuity confounds are further considered in the Discussion.