Item analysis
Analysis of NASH-CHECK item scores was conducted using baseline data to determine the final items for inclusion in NASH-CHECK and the optimal scale structure for scoring. Assessments included floor effects (percentage of patients reporting no symptoms or HRQOL impacts [i.e., scoring 0]) and ceiling effects (percentage of patients reporting the most severe symptoms or HRQOL impacts [i.e., the maximum possible score]), in addition to item correlations. Inter-item correlations were computed using Pearson correlation for the symptom items and polychoric correlation for the remaining items. Exploratory factor analysis (EFA) was conducted using Part B baseline data to determine a preliminary scale structure. For patients with missing baseline data, their NASH-CHECK responses at either week 6 or week 12 were used to maximize sample size for EFA. Models retaining varying numbers of factors (based on the initial analysis and guided by the initial conceptual model) were evaluated. Eigenvalues > 1 were used to identify separate factors, and chi-square tests for differences between alternative factor solutions were used to guide selection of the best-fitting factor models.Items were identified as candidates for removal from NASH-CHECK if they demonstrated redundancy (e.g., inter-item correlations > 0.8) or were poorly related to other items (e.g., inter-item correlations < 0.2 or EFA factor loadings < 0.3). A preliminary scale structure was determined based on the item-level analyses and subsequently confirmed using confirmatory factor analysis (CFA) using the Part C baseline data. Criteria to evaluate acceptable model fit were: chi-square test statistic P > 0.05; comparative fit index (CFI) ≥ 0.95; Tucker-Lewis index (TLI) ≥ 0.95; root mean square error of approximation (RMSEA) ≤ 0.06; standardized root mean square residual (SRMR) ≤ 0.08 (for symptom items); and weighted root mean square residual (WRMR) ≤ 1.0 (for HRQOL items) [