Analysis
NSAA
REGRESSION
Demographics at baseline were summarized. HUI3 and HUI2 attribute levels were calculated for each individual at baseline, 24 weeks, and 48 weeks (where this was possible, given availability of the required responses from the HUI questionnaire at each time point). An individual’s HUI responses could be unavailable due to incomplete, incorrect, or uninformative responses, or a missing assessment; no imputation was performed for visits with missing data. HUI3 and HUI2 utility values were estimated for each individual at these same visits following the developers’ algorithm and scoring instruction [To explore the relationship between the NSAA and other functional measures, correlations between NSAA score and the 6MWT, RFF grade, and 10MWR grade at baseline were explored. The correlation between 6MWT and HUI2 and HUI3 utility at baseline, and change in scores over time, was also calculated.Scatterplots with best-fitting lines were used to explore the relationship between NSAA score and HUI utility. Correlations were estimated between NSAA score and HUI score at baseline, as well as correlations between changes in NSAA and HUI scores over 48 weeks. The characteristics of individuals that contribute to variability in the estimate of correlation between NSAA and HUI scores was visually assessed. The correlation between change in NSAA scores and change in HUI3 and HUI2 attribute scores was also explored. A linear regression analysis was run to estimate the change in HUI score associated with a one-unit change in NSAA score. The percent of variation around mean changes in utility explained by changes in NSAA score (from baseline to week 48) was estimated using the RCorrelations between ordinal measures (NSAA with RFF or 10MWR), or utility and NSAA, were calculated as Spearman’s rank correlations. Pearson’s correlation was used to summarize correlations between utility and 6MWT. All analyses were conducted in R.