Sas stat software version

A one-way analysis of variance (ANOVA) was performed to test whether the means of bone biomarkers and bone parameters were equal between the four groups: STZ+DOX, STZ+CON, VEH+DOX, VEH+CON. Linear contrasts for the group variable were used to test the following four pairwise comparisons: STZ+DOX vs. STZ+CON, VEH+DOX vs. VEH+CON, STZ+DOX vs. VEH+DOX, and STZ+CON vs. VEH+CON. For each parameter, a step-down Bonferroni method was used to keep the overall family-wise error rate under 0.05 to adjust for multiple comparisons. An analysis of covariance (ANCOVA) model was used to compare mean cortical thickness (Ct.Th) between STZ+DOX and STZ+CON groups, after adjusting for RatLAPS. All tests were two-sided assuming a significance level of 5%. All statistical analyses were generated using SAS/STAT software, Version 9.4 of the SAS System for Windows 7, Copyright © 2002–2012 SAS Institute Inc.

We used descriptive analyses to summarize patient demographics and the prevalence of GA domain impairment. The associations of age and GA domain impairments with number of ICI cycles received, best response, incidence of irAEs, and hospitalization during ICI treatment were evaluated with the Wilcoxon rank sum test. All analyses were carried out using SAS/STAT^® software, Version 9.4 (SAS Institute Inc., Cary, NC, USA).

Demographic and clinical factors were summarized separately by PD-L1 (positive versus negative, high/moderate versus low) and PD-1 (positive versus negative, high/moderate versus low) status separately. High/moderate positivity was determined as an expression score of 3 or greater by IHC, and low expression of 1 or 2 by IHC. Selected factors (WHO grade, stage, smoker, MG diagnosis, and recurrence) were compared between groups using chi-square tests; p values were adjusted for multiple comparisons using Hochberg’s procedure. Estimates of OS were calculated using the Kaplan-Meier method and differences in the curves between groups were assessed using the log-rank test. In addition, hazard ratios (HRs) and 95% confidence intervals (CIs) from univariable (unadjusted) Cox proportional hazards models were calculated; due to the small sample sizes (both overall and number of events), additional covariate adjustment was not performed. All analyses were performed using SAS/STAT software, Version 9.4 of the SAS System for Windows (SAS Institute Inc., Cary, North Carolina).

Linear models were employed for all statistical comparisons. To examine the effects of treatment (iron or not), baseline ferritin, and other predictors on changes in TBI over the three 8-week intervals we employed a repeated measures model. Reduced forms of the model were employed to analyze subsets of the data, such as the effect of treatment on changes in a single interval. Student’s t-test was employed for comparing two means, such as the mean 24-week change in storage iron in those taking and not taking iron.
We used multiple imputation to account for the manner in which storage iron was calculated from ferritin alone. A separate normally distributed random variate with mean zero and standard deviation 0.80 was added to each estimate of storage iron to account for the residual uncertainty in the estimates. This involved creating 10 data sets with separate imputed values in each, analyzing each of the data sets and then combining the results to obtain final estimates and p-values.
Data analysis took place in SAS/STAT software Version 9.3 of the SAS System for Windows.