Baseline characteristics and visual acuity outcomes were compared across oedema categories using KruskalWallis one-way analysis of variance (continuous variables) and Pearson"s chi-square test (categorical variables). Pairwise comparisons were performed with the Student’s t-test (continuous variables) and Pearson’s chi-square test (categorical variables). Correlations between oedema duration/extent in years 1 and 2 were assessed using Pearson’s correlation. Probability values in the linear and logistic regression analyses were determined using Student’s t-test. Statistical analyses were performed with SAS versions 9.3 and 9.4 (SAS Inc., Cary, NC, USA). A P value of <0.05 was considered statistically significant.
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Lab products found in correlation
Sas 9, by SAS Institute (3 mentions)
R statistical software, by SAS Institute (2 mentions)
R software, by SAS Institute (2 mentions)
Spss software version 18, by IBM (2 mentions)
R version 3, by R Project for Statistical Computing (2 mentions)
Sas stat, by SAS Institute (2 mentions)
Spss statistics for window, by IBM (1 mentions)
R version 3, by SAS Institute (1 mentions)
Stata 17, by StataCorp (1 mentions)
Spss statistical package, by IBM (1 mentions)
194 protocols using sas version
1
Oedema-Based Visual Acuity Outcomes Analysis
2
Analyzing pSBI Incidence with GLMs
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Generalized linear models were used to assess differences between groups and to develop point and interval estimates for relative risk (RR) of the outcome of interest. Models were log binomial when possible; otherwise Poisson models were utilized. Generalized estimating equations were used to account for the correlation of outcomes within cluster to develop appropriate confidence intervals. In general, models included adjustment for randomization strata except that research site, rather than strata, was included in models assessing differences in the intervention group only (research question 4). For question 3, the proportions of infants with pSBI in clusters assigned to receive the intervention and in control clusters were compared during the pretrial period and during the trial period. Relative risks for pSBI during the trial period were estimated with adjustment for randomization strata only and again with adjustment for both strata and the pretrial proportions of pSBI at the cluster level. All tests were performed at a nominal significance level of α = 0.05. Due to the exploratory nature of the analyses, no correction was made for multiple comparisons. Additional statistical methods are noted under the results of each question, as needed. Analyses were done by RTI International with SAS versions 9.3 and 9.4 (SAS Institute, Cary, NC, USA).
3
Evaluating OS, PFS, and PPS in Treatment Groups
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The Kaplan–Meier (KM) method was used to estimate OS, PFS, and PPS, which were compared between the two treatment groups using the stratified log-rank test with a one-sided significance level of 0.025. Hazard ratio (HR; 95% confidence interval [CI]) was calculated using the stratified Cox proportional hazards model. A subgroup analysis of OS by PD-L1, TMB, and MSI status was performed with an unstratified Cox model, including HR and corresponding 95% CIs, to examine the effect of treatment on OS. A spider plot was presented to evaluate the change in tumor size compared to the first event of PD among patients with measurable lesions and imaging data during TBP. All analyses were performed using SAS versions 9.3 and 9.4 (SAS Institute, Inc., Cary, NC, USA).
4
Survival Analysis of Treatment Outcomes
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Sample size estimation has been described previously [2 (link)]. OS and PFS were compared between the treatment groups using the stratified log-rank test with a one-sided significance level of 0.025. Hazard ratio [HR; 95% confidence interval (CI)] was calculated using the stratified Cox proportional hazards model. The Kaplan–Meier method was used to estimate the median OS and median PFS, and for the subanalysis of OS by BOR and by tumor PD-L1 expression status. For the landmark analysis, standard OS curves were generated for patients found to have SD at the first evaluation (6 weeks), and the patients were categorized into three groups based on the tumor growth rate at 6 weeks. All analyses were performed using SAS versions 9.3 and 9.4 (SAS Institute, Inc., Cary, NC, USA).
5
Evaluating Risk Factors using GLMs
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Generalized linear models were used to evaluate the relationship between covariates and the outcomes of interest and to develop point and interval estimates of relative risk (RR) associated with these risk factors. Generalized estimating equations were used to account for the correlation of outcomes within cluster to develop appropriate confidence intervals. Models were log binomial when possible, otherwise Poisson models were utilized. Models were adjusted for randomization strata. All analyses were done by RTI International with SAS versions 9.3 and 9.4 (SAS Institute, Cary, NC, USA).
6
Gender-Specific Glycemic Outcomes with Gla-300
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All outcome measures were analysed according to gender. The changes in HbA1c and FPG from baseline to weeks 12 and 24 of Gla-300 treatment were analysed using a mixed model for repeated measures, with fixed categorical effects of visit and gender as well as continuous fixed covariates of baseline HbA1c or FPG, baseline HbA1c or FPG value-by-visit interaction and gender-by-visit interaction. For these two efficacy endpoints, the least square (LS) mean differences between men and women and two-sided 95% confidence intervals (CIs) were estimated.
All other efficacy and safety endpoints as well as baseline demographic and disease characteristics were assessed descriptively, with categorical variables presented as counts and percentages, and continuous variables as mean, standard deviation (SD), median, and first and third quartiles.
Efficacy and safety assessments were based on all included patients who received at least one Gla-300 dose. There were missing patient baseline characteristics and missing outcome data in some studies; no imputation of missing data was performed. All statistical tests were two-sided, with a p value < 0.05 considered statistically significant. All analyses were performed using SAS, version 9.4 (SAS Institute Inc., Cary, NC, USA).
All other efficacy and safety endpoints as well as baseline demographic and disease characteristics were assessed descriptively, with categorical variables presented as counts and percentages, and continuous variables as mean, standard deviation (SD), median, and first and third quartiles.
Efficacy and safety assessments were based on all included patients who received at least one Gla-300 dose. There were missing patient baseline characteristics and missing outcome data in some studies; no imputation of missing data was performed. All statistical tests were two-sided, with a p value < 0.05 considered statistically significant. All analyses were performed using SAS, version 9.4 (SAS Institute Inc., Cary, NC, USA).
7
Tonsillectomy and Cardiovascular Disease Risk
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To compare the general characteristics between the tonsillectomy and control groups, the absolute standardized difference (SD) was employed. An absolute SD below 0.20 was deemed a sign of achieving satisfactory balance [22 (link)].
Stratified Cox proportional hazard models were utilized to assess the hazard ratios (HRs) and 95% confidence intervals (CIs) of tonsillectomy for CVD. In this analysis, both crude (simple) and adjusted models (factoring in obesity, smoking status, alcohol consumption, SBP, DBP, fasting blood glucose, total cholesterol, and hemoglobin levels, and CCI scores) were employed. The analysis was stratified by matching variables, including age, sex, income, and region of residence. Kaplan–Meier curves were generated, and log-rank tests were conducted.
For subgroup analyses using the stratified Cox proportional hazards model, participants were divided by age (<50 years old and ≥50 years old), sex (male and female), income (low and high), and region of residence (urban and rural).
Two-tailed analyses were conducted, with significance defined as a p-value less than 0.05. Statistical analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA).
Stratified Cox proportional hazard models were utilized to assess the hazard ratios (HRs) and 95% confidence intervals (CIs) of tonsillectomy for CVD. In this analysis, both crude (simple) and adjusted models (factoring in obesity, smoking status, alcohol consumption, SBP, DBP, fasting blood glucose, total cholesterol, and hemoglobin levels, and CCI scores) were employed. The analysis was stratified by matching variables, including age, sex, income, and region of residence. Kaplan–Meier curves were generated, and log-rank tests were conducted.
For subgroup analyses using the stratified Cox proportional hazards model, participants were divided by age (<50 years old and ≥50 years old), sex (male and female), income (low and high), and region of residence (urban and rural).
Two-tailed analyses were conducted, with significance defined as a p-value less than 0.05. Statistical analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA).
8
Comprehensive Statistical Analysis of Acceleration and Bruising
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Statistical Analysis Software (SAS version 9.2, SAS Inst. Inc., Cary, NC,) was used to determine the descriptive statistics using the MEANS procedure and the regression equations for calibration using the REG procedure. The Mixed procedure in SAS 9.2 was used to determine the fixed effects of compartment (experimental unit) on both acceleration (n = 8) and bruising severity (n = 7) using a completely randomized block design with load as a random effect. Loading density was added as a covariate to the model but was removed because it was not significant (P value > 0.10). A quadratic multiple regression in SAS was used to determine the relationship between acceleration and bruising severity with load as a random effect. Linear regressions were performed to compare the output from the two types of accelerometers (X16-1C and 352C65) by determining the intercept and slope of the regression line. A general linear model (GLM) was used to determine if the slopes and intercepts varied between the two types of accelerometers. Kappa values for inter-observer reliability were 0.63 (P < 0.01) and the observers agreed on 99.5% of the bruise locations. Statistical significance was established at P < 0.05 and trends at 0.05 < P ≤ 0.11.
9
Comparing Artificial Disc Replacement and Fusion Outcomes
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To compare ALD outcomes between TDR and fusion, MAICs were conducted. Detailed methods are provided in the supplementary appendix. Briefly, individual patient-level data for TDR from the activL randomized trial17 were matched and adjusted with summary data for the fusion arm from the Zigler et al,16 (link) study. After aligning inclusion/exclusion criteria between the two studies, baseline characteristics were compared and adjusted for any imbalances (i.e., age, body mass index, sex, smoking status, index level, blood loss, and hospital stays; see Supplementary Tables). Results are presented as odds ratios (ORs) and 95% confidence intervals (CIs). Sensitivity analyses using an anchored MAIC approach were also performed to determine the benefit of activL compared with fusion on ALD and to validate the findings of the unanchored MAIC (detailed methods presented in Supplementary Appendix). Analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC) and R version 3.3.1. (R Development Core Team, University of Auckland, New Zealand).
10
Biomarkers and Emergency Department Delirium
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All biomarker measurements were log transformed to minimize the influence of extreme outliers. Proportional odds logistic regression was performed to examine the independent association of biomarkers of inflammation, coagulation, endothelial activation, and BBB injury with ED delirium duration. These models were adjusted for dementia, baseline functional status, comorbidity burden, severity of illness, CNS diagnosis, infection diagnosis, and use of home steroids. These covariates were chosen a priori based upon literature review and expert opinion. Since dementia is associated with many of these mechanistic pathways and with delirium, we also evaluated if the association between biomarkers and ED delirium duration was modified by dementia. We allowed for potential interactions between biomarkers and dementia using a separate cross-product interaction term. Because this analysis was exploratory in nature, we considered effect modification to be present if the interaction term’s p-value was less than 0.20. Proportional odds ratios with their 95% confidence intervals (95%CI) were reported. All statistical analyses were performed with SAS version 9.4 (SAS Institute, Carey, NC) and open source R statistical software, version 3.0.2 (http://www.r-project.org/ ).
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