Sas stat software version 9

Manufactured by SAS Institute

Sourced in United States, Cameroon

SAS/STAT software version 9.4 is a comprehensive statistical analysis package designed to analyze data and perform advanced statistical modeling. It provides a wide range of statistical procedures and techniques for data management, exploration, modeling, and inference. SAS/STAT 9.4 supports a variety of statistical methods, including regression analysis, analysis of variance, multivariate analysis, survival analysis, and more.

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Sas system for windows, by SAS Institute (22 mentions) Sas system for windows 7, by SAS Institute (2 mentions) Spss release 17, by IBM (2 mentions) Prism v6.0d, by GraphPad (2 mentions) Vectra system, by PerkinElmer (2 mentions) Sas base, by SAS Institute (2 mentions) Sas system for linux, by SAS Institute (2 mentions) Sas system for pc, by SAS Institute (2 mentions) Axio scope a1 compound microscope, by Zeiss (2 mentions) Spss pc release 17, by IBM (2 mentions) Prism 5, by GraphPad (2 mentions) Proliant ml350 server, by Hewlett-Packard (2 mentions) Prism 6, by GraphPad (2 mentions) Megascript, by Thermo Fisher Scientific (1 mentions) R software, by R Project for Statistical Computing (1 mentions) Sigmaplot for windows version 12, by Grafiti LLC (1 mentions)

109 protocols using sas stat software version 9

Hypnosis for Anxiety Reduction in Coronary Angiography

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The sample size (85 patients per group) necessary to reach a power of 90% with an alpha risk of 5%, was calculated from the literature, which made it possible to estimate an expected score of 50 on STAI-Y A (standard deviation 10) for the control group and a clinically relevant difference of 5 points between the two groups 26,27.
The results are presented as median and ranges for qualitative data or as numbers and percentages for quantitative data. The hypnosis (experimental) and control groups were defined by intention to treat. The comparison between the two groups was carried out using Wilcoxon tests for the quantitative variables and Fisher's exact tests for the qualitative variables. A multivariate generalized linear model was used to identify factors that may influence the STAI Y A score before coronary angiography (age, sex, group, anxiety trait, anxiety state the day before, belief in the effectiveness of hypnosis in general and belief in the effectiveness of hypnosis for its own sake). The significance threshold was set at 5%. Bonferroni's corrections were applied for multiple tests where applicable. All analyzes were performed with SAS/STAT software version 9.4 (SAS Inst., Cary, NC).

Abensur Vuillaume L., Gentilhomme C., Weber S., Ouamara N., Bayard J., Valla M., Khalife K., Goetz C, & Guler N. (2022). Effectiveness of Hypnosis for the Prevention of Anxiety During Coronary Angiography (HYPCOR study): a prospective randomized study. BMC Complementary Medicine and Therapies, 22, 315.

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Trends in Antibiotic Use: A Quality Improvement Study

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This manuscript adheres to the standards for QI excellence (SQUIRE) 2.0 guidelines (22 (link), 23 (link)). Descriptive statistics included mean values and standard deviation (SD) for normally distributed continuous variables, and absolute and relative frequencies for categorical variables. Trends of antibiotic use and of characteristics for the study period (2014–2019) were analyzed using the Cochran-Armitage Trend test. Change over time in continuous variables was assessed using analysis of variance (ANOVA). A p-value of < 0.05 was considered statistically significant. SAS/STAT software, Version 9.4 (SAS Institute Inc., Cary, NC) was used for analyses.
Statistical process control charts (SPC) were created (QI Macros, KnowWare International, Inc, Denver, CO) to display and analyze data over time. The following rules were used to determine special cause variation (SCV): one point outside the upper or lower control limits, two of three points beyond 2 SD from the mean on the same side of the center line (CL), four of five successive points beyond 1 SD from the mean on the same side of the CL, eight successive points on the same side of the CL, or six successive increasing or decreasing points (24 (link)).

Morales-Betancourt C., De la Cruz-Bértolo J., Muñoz-Amat B., Bergón-Sendín E, & Pallás-Alonso C. (2022). Reducing Early Antibiotic Use: A Quality Improvement Initiative in a Level III Neonatal Intensive Care Unit. Frontiers in Pediatrics, 10, 913175.

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Insulin Sensitivity in Crossover Study

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The minimum group size (n = 24) was calculated to provide 80% power to detect an anticipated difference of 11% in insulin sensitivity over 6 wk, as measured by the Matsuda index (SD: 1.6), at P < 0.05 (21 (link)). The recruitment goal was fixed at 33 participants to account for 20–25% dropout.
Statistical analyses were conducted using SAS/Stat software version 9.4 (SAS Institute Inc.). Skewness (±1) and/or kurtosis (±4) were used to assess the normality of distribution. Variables were transformed using the log10 or squared root in case of abnormal distribution. Comparison of baseline characteristics between participants who dropped out and those who completed the study was conducted using 2-sample independent t tests and chi-squared tests. Comparison between groups was conducted using a mixed model with repeated measures for crossover designs (22 (link)). The model included the variables treatment (HD or AD), visit number (1–4), and selected covariables (age, sex, and BMI) as fixed effects. Subjects were included as the random effect and visits were included in the repeated statement. The interaction treatment × visit was tested for all dependent variables. Multiple comparisons between visits were conducted using Tukey's post hoc test. Data are expressed as arithmetic means ± SDs unless otherwise stated, with statistical significance set at P < 0.05.

O'Connor S., Julien P., Weisnagel S.J., Gagnon C, & Rudkowska I. (2019). Impact of a High Intake of Dairy Product on Insulin Sensitivity in Hyperinsulinemic Adults: A Crossover Randomized Controlled Trial. Current Developments in Nutrition, 3(8), nzz083.

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Prognostic Impact of ATM Status in Lung Cancer

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Overall survival (OS) was measured from the date of diagnosis to the date of death, or censored at the last date the patient was known to be alive. The primary analysis used Kaplan-Meier methods and the log-rank test to assess the univariate relationship between ATM status and OS. Multivariable Cox proportional hazards models were fitted to evaluate ATM in addition to known prognostic factors such as age, sex, and pathologic stage. For patients with multicentric disease, pathology could be updated from cancer registry entries to reflect the highest stage for synchronous disease. This reflected the use of pathology stage as a covariate reflecting the patient's prognosis rather than an independent marker for the tissue present in the TMA. Sample size was determined by the available tissue and follow-up events in the TMA, which was assembled for use by multiple University of Pittsburgh lung SPORE projects. Statistical analyses were conducted using SAS/STAT software, version 9.4 (SAS Institute, Inc., Cary, NC), with all p-values two-sided.

Villaruz L.C., Jones H., Dacic S., Abberbock S., Kurland B.F., Stabile L.P., Siegfried J.M., Conrads T.P., Smith N.R., O'Connor M.J., Pierce A.J, & Bakkenist C.J. (2016). ATM protein is deficient in over 40% of lung adenocarcinomas. Oncotarget, 7(36), 57714-57725.

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Intergenerational Impacts of Parental AUD

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Differences between children whose parents did and did not have AUD were tested with t- and chi-square tests. Cox regression models with 95% confidence intervals were used to compare the mortality in children whose parents did and did not have AUD. Follow-up started at birth and ended at death, migration from Sweden, or December 31, 2018, whichever came first.
Mortality rates were calculated per 100,000 follow-up years. After confirming that the assumptions for Cox regression were fulfilled, both crude and background-adjusted hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated. HRs were adjusted for year of birth, sex, highest level of parental education, and loss of ≥ 1 parent before the child reached the age of 18 years. A sensitivity analysis that included only the first births of each mother-father pair (firstborns) was performed to test whether the inclusion of siblings (dependent observations) affected the results of the Cox regression.
The data analysis for this paper was generated using SAS/STAT software, Version 9.4 of the SAS System for Windows 7 (SAS Institute Inc., Cary, NC, USA).

Westman J., Jayaram-Lindström N., Kane K., Franck J, & Gissler M. (2022). Mortality in adult children of parents with alcohol use disorder: a nationwide register study. European Journal of Epidemiology, 37(8), 815-826.

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Comparison of Rotator Cuff Repair Methods

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Baseline patient characteristics were summarized using standard descriptive statistics: mean, minimum, maximum, and standard deviation (SD) for the continuous variables; and frequency and percent for the categorical variables. Differences in the characteristics between WoC and WC cohorts were tested using the Wilcoxon Mann-Whitney test for the continuous variables and Fisher’s exact test for the categorical variables. The proportions of PCF between the medial and lateral sides were calculated and compared using mixed Poisson regression models. This approach accounts for the fact that incidence of PCF is assessed in a double row repair model comparing both ASAs and PLLA anchors within the same patient; this is in contrast to a more simplistic approach that does not account for placement of ASAs and PLLA anchors within the same patient, for example, a McNemar’s paired test. The point estimates and their corresponding 95% confidence intervals were calculated. The statistical tests were two-sided at a significance level of 0.05. The analyses were performed using the SAS/STAT software, version 9.4, of the SAS System for Windows (copyright 2016 SAS Institute Inc.).

Matijakovich D., Solomon D., Benitez C.L., Huang H.H., Poeran J., Berger N., Lebaschi A, & Seneviratne A. (2021). Long-term follow-up of perianchor cyst formation after rotator cuff repair. JSES International, 5(5), 863-868.

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Hemodynamic and Cytokine Response to HVHF

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The study was powered to detect a 4.8-unit difference in the drop of the primary endpoint from baseline at 90% power, with a type I error rate of 5% resulting in a required sample size of 120 subjects.
Continuous data are summarized as the median (25th, 75th quantile) while categorical data are summarized as proportions. Fisher’s exact test, McNemar’s test, and the Wilcoxon rank-sum test were used as appropriate. Hemodynamic parameters were compared between controls and HVHF at both hour 0 and hour 48. Median values within each group were then compared between hour 0 and hour 48 to assess the difference in the drop of the VDI from baseline. To control the type I error rate for each variable at 0.05 given four statistical tests, an alpha level of 0.0125 was used to determine significance.
Linear mixed-effect models were used to compare trends in cytokine values over the first 48 hours between the control and HVHF groups. A random intercept and slope term was included for each subject.
Data were analyzed following the intention-to-treat principle where appropriate. All tests were two-sided at a significance level of 0.05. Analyses were conducted using R Version 3.3.1 (R Core Team) or SAS/STAT software version 9.4 (SAS Institute, Inc.).

Chung K.K., Coates E.C., Smith DJ J.r., Karlnoski R.A., Hickerson W.L., Arnold-Ross A.L., Mosier M.J., Halerz M., Sprague A.M., Mullins R.F., Caruso D.M., Albrecht M., Arnoldo B.D., Burris A.M., Taylor S.L, & Wolf S.E. (2017). High-volume hemofiltration in adult burn patients with septic shock and acute kidney injury: a multicenter randomized controlled trial. Critical Care, 21, 289.

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Multivariate Analysis of Clinical Factors

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Categorical variables were presented as proportions while comparisons
were performed using the Chi-square or Fisher’s exact test where
appropriate (< 5 cases). Continuous variables are presented as medians
with inter-quartile ranges (IQR). Univariate comparisons were performed using
the Kruskall-Wallis test. Statistical significance was defined as a p value
< 0.05 for all comparisons. Multivariate analysis was performed with
logistic regression. Covariates initially considered for modeling were chosen
based on clinical relevance or a p<0.10 on univariate comparisons.
Collinearity was determined and avoided where appropriate. Final model
performance was assessed by c-statistic. All statistical analysis were done
using SAS-STAT software, Version 9.4 (SAS Institute Inc., Cary, North Carolina,
US).

Karvellas C.J., Cardoso F.S., Gottfried M., Reddy K.R., Hanje A.J., Ganger D, & Lee W.M. (2016). HBV-associated Acute Liver Failure after Immunosuppression and Risk of Death. Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association, 15(1), 113-122.

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Improving Guideline Adherence for MI Diagnosis

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Microsoft Excel (Version 2010, Redmond, Washington) and JMP (SAS Institute Inc, Cary, North Carolina) were used for data trending, classification, distribution analysis, graphing, and dashboard reports. Creatine kinase-only orders (not containing cTnI or CKMB) were omitted as not related to the MI diagnosis. Orders were classified as “guideline” (cTnI-only orders) or “nonguideline” (any other included combination). Because diagnostic volume varied, results are presented as percentage adherent or nonadherent to guidelines. Differences in percentages across time and across groups were tested using χ² tests of association. A sensitivity analysis used generalized linear mixed effects models (GLIMMs) with linear splines, assuming that the distribution of guideline orders followed a binomial distribution and was correlated over time within groups, in order to test whether the probability of having a guideline order was different immediately before and after staged intervention components or preintervention/postintervention. Analyses used SAS/STAT software, version 9.4 of the SAS System for Windows (^© 2012 SAS Institute Inc, Cary, North Carolina).

Ducatman A.M., Tacker D.H., Ducatman B.S., Long D., Perrotta P.L., Lawther H., Pennington K., Lander O., Warden M., Failinger C., Halbritter K., Pellegrino R., Treese M., Stead J.A., Glass E., Cianciaruso L, & Nau K.C. (2017). Quality Improvement Intervention for Reduction of Redundant Testing. Academic Pathology, 4, 2374289517707506.

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Myocardial Contrast Imaging Comparison

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Since the presence or absence of myocardial contrast at high and low mechanical indices were obvious and consistent, comparisons of the presence or absence of myocardial contrast using the different imaging techniques utilized to activate the intravenously infused C3 versus C3C4 droplets were descriptive and not determined with contingency tables. When comparing myocardial contrast intensity between real time and triggered frame rates in the different animals (n=18 comparisons in three animals), a covariance structure which treats random variation due to the three pigs was performed, which then treats the paired data with a 2×2 unstructured co-variance matrix. The average size of any visualized perfusion defect (at rest and during repeat LAD occlusion) with any of the imaging techniques was planimetered off line, and compared with post mortem EB and TTC staining using a Spearman correlation with an exact p value to summarize the association. The planimetered size of any defect was also compared with the number of segments (using a 16 segment model) exhibiting transmural (>50% transmural thickness) delayed enhancement on post gadolinium magnetic resonance imaging using a Spearman correlation coefficient. Statistical analyses were generated with SAS/STAT software, Version 9.4 (© 2002 – 2012 SAS Institute Inc.)

Porter T.R., Arena C., Sayyed S., Lof J., High R.R., Xie F, & Dayton P.A. (2016). Targeted Transthoracic Acoustic Activation of Systemically Administered Nanodroplets to Detect Myocardial Perfusion Abnormalities. Circulation. Cardiovascular imaging, 9(1), 10.1161/CIRCIMAGING.115.003770 e003770.

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