Sas version 9

Statistical analyses were performed using GraphPad Prism version 8 (La Jolla, CA), SAS version 9.2 (Cary, NC), and SPSS version 22.0 (SPSS Inc., Chicago, IL, USA). The primary outcome variable of the open-label RLD clinical trial was the change in global NASH scores. Two-tailed P value of < 0.05 was considered significant as was decided a priori. A chi-square test was used to compare categorical variables. The independent samples t-test was used to compare independent groups. The repeated-measures ANOVA was used to compare variables that were based on repeated observations. Differences in each collected parameter were evaluated using a paired test (compared to baseline). P values are marked if they are significant after multiplicity correction. Paired t-test was used to compare month-12 values to baseline (without multiplicity correction) as the change at 12 months vs. baseline was a prespecified endpoint. Different components of the NASH score before and after metreleptin treatment were compared by Wilcoxon matched-pairs signed rank test. Log transformation was applied if needed. If data were skewed, nonparametric tests were used as needed.

Data were analyzed using SAS version 9.2 and SPSS version 21. In all analyses, p < 0.05 was considered statistically significant. The following variables were analyzed using a three-way analysis of variance (ANOVA), with genotype (transgenic vs. non-transgenic), exercise (runner vs. sedentary), and chow (valganciclovir vs. control) as the three factors, with all interactions entered in the model: chow intake (grams/day), valganciclovir dose (mg/kg/day) and duration (s) in the target quadrant of the MWM during the probe trial. In Experiment 1, the following dependent variables were transformed to improve assumptions of normality: total number of BrdU-positive cells (square root) in the dentate gyrus, and average distance run on wheels (log). In some cases, where stated, data were also analyzed by two-way ANOVA to determine genotype contributions to certain effects (e.g. within transgenic and non-transgenic groups for running levels, etc.).

The mid-sagittal plane of the CC and regions-of-interest were obtained from raw 3D MR images using the Yuki module within the Automated Registration Toolbox (ART) for CC segmentation (Ardekani, 2013 ). Briefly, identification of the mid-sagittal plane was performed for all subjects using a reliable algorithm (Ardekani et al., 1997 (link)), which was followed by automated identification of the anterior commissure (AC) and posterior commissure (PC) (Ardekani & Bachman, 2009). Individual voxels belonging to the CC were identified through automated comparisons between each voxel of the mid-sagittal slice and 49 brain atlases that were registered to the image based on AC-PC alignment (Ardekani et al., 2012 ). All voxels identified as belonging to the CC were labeled as one contiguous structure, extracted and saved in NIFTI image format. The NIFTI image of each midsagittal CC area was visually inspected and manually edited when necessary by an investigator (DP) blind to participant characteristics using ITK-Snap (Yuskevich et al., 2006 (link)). The NIFTI images conformed to the CC borders of the original mid-sagittal magnetic resonance imaging cross section. Following editing, measurement parameters of the CC were extracted using ART (Ardekani, 2013 ). Output data were analyzed using IBM SPSS version 21.0, SAS version 9.2, and SPSS AMOS version 16.

Categorical variables were summarized using proportions, and continuous variables were expressed as the mean (± standard deviation) and median (range). Characteristics of patients with residual and no-residual disease were compared using the Fisher exact test for categorical variables and Wilcoxon signed rank test for continuous variables. Survival curves were constructed by the Kaplan-Meier method and were compared using the log-rank test. A univariate Cox proportional hazards regression model was used in all patients treated with complete resection (N=116) to identify factors individually predictive of residual disease and to determine prognostic factors associated with DFS and DSS. All variables that were significant at the 10% level on univariate analysis were entered into a multivariate model using logistic regression, with two exceptions to avoid problems of collinearity: (1) any RD, RD in the liver, bile duct, lymph nodes, or at other sites, and the number of sites affected for RD were highly correlated and only any RD was considered in the Cox model; (2) T, N, and M stages and the overall TNM stage were highly correlated and only TNM stage was considered in the Cox model. All tests were two-sided and statistical significance was defined as p <0.05. Statistical analysis was performed with SAS version 9.2 and S.P.S.S version 19.0.