Variation among hospitals was estimated by a generalized mixed‐effect model with institution as a random effect after adjusting for age, sex, comorbidity, tumor location, body mass index (BMI), and clinical stage. A logistic regression model was used for binomial outcomes, and the Cox regression model was used for time‐to‐event outcomes. The associations between the estimated outcomes and hospital/surgeon volume were evaluated by Spearman's correlation coefficients. All statistical analyses were performed with SAS v. 9.2 or higher (SAS Institute, Cary, NC).
Sas v 9.2 or higher
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SAS v. 9.2 or higher is a software suite developed by SAS Institute for advanced analytics, data management, and business intelligence. It provides a comprehensive set of tools and capabilities for data analysis, modeling, and reporting. The core function of SAS software is to enable users to access, manipulate, analyze, and visualize data from various sources, supporting decision-making and business insights.
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Lab products found in correlation
4 protocols using sas v 9.2 or higher
1
Estimating Variation in Surgical Outcomes
2
Comparison of Estrogen and Progesterone Doses
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Percent changes from baseline to months 6 and 12 for each BTM marker were calculated and compared between the 1/100 and 0.5/100 E2/P4 doses and placebo. P values were calculated for between-group comparisons using the mixed model for repeated measures with treatment, visit, and treatment-by-visit interaction as factors; baseline as covariate; and subject as repeated measure unit. Statistical analysis was performed with SAS v 9.2 or higher (SAS Institute, Cary, NC).
3
Univariable and Multivariable Analysis of OS and RFS
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OS and RFS from the time of surgery were assessed using a univariable and multivariable Cox regression model adjusted by the clinical and pathological factors for which the P‐value was <.3 by Fisher's exact test between the two arms. In this study, clinical and pathological factors were not always the same for T and N factors; we judged that it did not affect the model estimation and these were used as adjustment factors. All P‐values were two‐sided, and statistical analysis was performed using SAS v. 9.2 or higher (SAS Institute, Cary, NC).
4
Noncompartmental PK Analysis and Bioequivalence
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Noncompartmental analyses of PK data and statistical analyses of bioequivalence and dose proportionality were performed using Phoenix WinNonlin v6.3 or v6.4 (Certara, Princeton, NJ). Descriptive statistics were calculated for quantitative parameters using SAS® v9.2 or higher (SAS Institute, Inc., Cary, NC). For bioequivalence evaluations, a mixed-effects model analysis based on the U.S. FDA Guidance for Industry (2001) “Statistical Approaches to Establishing Bioequivalence” was performed on the natural logarithmic (Ln) transformation of the primary PK exposure metrics Cmax and AUC0-t. The mixed-effects model included sequence, period, and treatment as fixed effects and subject as a random effect. For the dose proportionality analysis, DN Cmax and AUC0-t values from Study I (Part 1) were used for the bioequivalence evaluation, as described above. Per FDA guidance, exposure equivalence was concluded if the 90% confidence intervals (CIs) for the test/reference ratio of geometric LS means for the Ln-transformed Cmax and AUC0-t fell within the bioequivalence limits of 0.8–1.25 (US Food and Drug Administration 2001 ).
All participants who received at least one dose of study drug were included in the safety population. The PK population was defined as all randomized participants who received study drug and for whom the PK profile could be adequately characterized.
All participants who received at least one dose of study drug were included in the safety population. The PK population was defined as all randomized participants who received study drug and for whom the PK profile could be adequately characterized.
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