Statistical Analyses

We used the median and IQRs to describe the quantitative variables, absolute frequencies, and the proportions of the qualitative variables. We used mixed-effects regression to model the rate of change in brain volume, accounting for intraparticipant correlation.²⁴ We compared third-order and second-order B-spline and linear models based on Akaike Information Criterion.²⁵ With disease duration (time from MS onset) as a main fixed-effect predictor, these models were used to fit the rates of brain volume changes between visits using the JI and FSL methods. We used the pseudoR² defined by Nakagawa and Schielzoth²⁶ to estimate the goodness of fit of the models. To evaluate the potential influence of sex, age, DMT use, and use of steroids, we included these variables as fixed effect predictors in mixed-effects models. We included age at MS onset instead of age at study inclusion to avoid collinearity with disease duration. For variables with a significant association, we evaluated whether the effect on the rate of brain volume change might differ during disease progression (effect modification) using an interaction term between MS duration and covariate.

Short-term test-retest measurement errors were estimated using the formula of 100 * (V2 − V1) / mean(V1,V2), where V1 and V2 were absolute values measured at the 2 time points for the segmentation technologies (SIENA-X and FIRST), while the absolute percentage change in volume between 2 points was used for registration-based technologies (SIENA and JI). We presented the 50th and 75th percentiles with their 95th CIs estimated using a bias-corrected accelerated bootstrap to summarize the test-retest measurement errors. We also calculated the same difference after coregistering both MPRAGE (which is not a standard step for SIENA-X).

To assess if the JI and FSL estimated brain volumes are comparable, R² was evaluated as a measure of the goodness of fit of the simple linear regression models for pairwise brain regions quantified with JI and FSL. Additionally, we evaluated the performance of the annual rate of whole and regional brain volume changes to classify 20 healthy volunteers and 100 age and sex-matched patients with MS. We used a Hosmer-Lemeshow goodness-of-fit test for calibration and receiver operating characteristics curve analyses for discrimination. We also obtained the area under the curve, sensitivity, and specificity of the best cutoffs according to the Youden J statistic.²⁷

We performed 2 sensitivity analyses: complete case analyses to assess the influence of missing data and analyses excluding participants with more than a 10-fold interscan change (which would evaluate the influence of extreme values on results). Two-tailed P values <.05 were considered statistically significant, and all statistical analyses were performed using R language (R version 3.3.3; R Foundation for Statistical Computing). Data analysis was completed from January 2017 to May 2017.