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Sas system

Manufactured by SAS Institute
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The SAS System is a comprehensive software suite for advanced analytics, data management, and business intelligence. It provides a robust and flexible platform for data processing, statistical analysis, and predictive modeling. The core function of the SAS System is to enable users to access, manipulate, and analyze data from a variety of sources, and to create reports, visualizations, and models to support decision-making.

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Lab products found in correlation

Sas software, by SAS Institute (22 mentions) Prism 6, by GraphPad (13 mentions) Prism 8, by GraphPad (2 mentions) Sas 9, by SAS Institute (2 mentions) Sas system for windows, by SAS Institute (2 mentions) Sigmaplot, by Grafiti LLC (1 mentions) Spss for window, by IBM (1 mentions) R statistical software, by R Project for Statistical Computing (1 mentions) Spss 24.0 for windows, by IBM (1 mentions) Spss software, by IBM (1 mentions) Sigmaplot 14, by Grafiti LLC (1 mentions) Jmp pro 13, by SAS Institute (1 mentions) R version 3, by R Project for Statistical Computing (1 mentions) Sas enterprise guide sas eg 7.1, by SAS Institute (1 mentions) Spss statistics for window, by IBM (1 mentions) Spss statistic, by IBM (1 mentions) Spss v25, by IBM (1 mentions) Sas software 9, by SAS Institute (1 mentions) Sigmaplot 9, by Grafiti LLC (1 mentions) Quantity one software, by Bio-Rad (1 mentions)

230 protocols using sas system

1

Cephalometric and Polysomnographic Analysis

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Descriptive analysis based in contingency tables and correlated Chi-square tests and basic statistics was used to characterize the sample. Generalized linear mixed model for repeated measure and post hoc Tukey–Kramer test compares the variables pre- and post-treatment. Residual normality was accessed by the Shapiro–Wilk Test and Pearson correlation coefficients was used to test and quantify the association between polysomnographic and cephalometric data. All analysis was calculated by using the SAS System (SAS Institute Inc. The SAS System, release 9.4. SAS Institute Inc., Cary, North Carolina, United States, 2012) and in all statistical tests the level of significance was set in 5%.
Barbosa D.F., Meira e Cruz M., Alves M.C., Zancanella E., Berzin F, & Júnior A.J. (2022). Effectiveness of a Uniquely Designed Oral Appliance on Obstructive Sleep Apnea Control: A Pilot Study. European Journal of Dentistry, 16(3), 564-572.
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2

Generalized Linear Mixed Model for Wire Adherence

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The data were analyzed with the SAS System (SAS Institute Inc. The SAS System, release 9.2. SAS Institute Inc., Cary, NC, USA, 2008). Response variables did not fulfill the assumptions of equality of variances and normal distribution of errors. Thus, a generalized linear mixed model was adjusted for log-normal distribution for an experiment with a random effect (volunteers) for fixed-effect test (wire). After transformation, the adherence of the residuals was evaluated by the Shapiro-Wilk test. These transformed variables were analyzed by ANOVA and Tukey-Kramer test for multiple comparisons of means, if significant effect was found. The significance level was fixed at 0.05.
Shirozaki M.U., Ferreira J.T.L., Küchler E.C., Matsumoto M.A.N., Aires C.P., Nelson-Filho P, & Romano F.L. (2017). Quantification of Streptococcus mutans in Different Types of Ligature Wires and Elastomeric Chains. Brazilian dental journal, 28(4).
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3

Evaluating Breeding Strategies in Cows

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Baseline comparisons were established evaluating the distribution of cows in both groups using a Chi-square test (Proc Freq, SAS system ® ). The effect of treatment group (5-d P+CoS vs.
7-d P+E), breeding season (1, 2, 3 and 4), estrous cycle status (anestrus vs. cyclic), and sire (A, B, C, D, E, F, G and H) on pregnancy per AI was determined by univariate analysis with a Chisquare test and multivariable analysis using the backward elimination procedure (Proc Logistic, SAS system ® ) of multiple logistic regression [23] . The effect of treatment group, experimental day, and their interaction on follicular dynamics was evaluated using analysis of variance using the repeated measures method(Proc Mixed, SAS system ® ) using treatment group and the interaction treatment group and day as fixed variables, cow nested in treatment group as random
Bilbao M.G., Zapata L.O., Romero Harry H., Perez Wallace S., Farcey M.F., Gelid L., Palomares R.A., Ferrer M.S, & Bartolome J.A. (2019). Comparison between the 5-day cosynch and 7-day estradiol-based protocols for synchronization of ovulation and timed artificial insemination in suckled BOS taurus BEEF cows. Theriogenology, 131.
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4

Statistical analysis of experimental data

Cited 1 time
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Firstly, an exploratory analysis of the data was performed. Continuous variables
with normal distribution are reported as mean + standard deviation and
categorical variables are presented as absolute numbers and percentages. The
Fisher exact test was used to compare categorical variables, the McNamer's test
was used to evaluate the effect of the intervention and ANCOVA was proposed to
compare the groups and to verify the effect of the covariates.22 This analysis assumes that its
residues have a normal distribution with mean 0 and variance s2 (link) constant. Transforms were used
in response variables that did not reach the assumption. Differences were
considered to be statistically significant when p < 0.05. The SAS system
(version 9; SAS Institute, Cary, NC) was used for all statistical
calculations.
Villela P.T., de-Oliveira E.B., Villela P.T., Bonardi J.M., Bertani R.F., Moriguti J.C., Ferriolli E, & Lima N.K. (2019). Salt Preference is Linked to Hypertension and not to Aging. Arquivos Brasileiros de Cardiologia, 113(3), 392-399.
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5

Statistical Analysis of Experimental Data

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Data were presented as the mean ± standard error of the mean (SEM). Statistical significance was determined using the SAS System (SAS Institute). Statistical analysis was performed using an F-test followed by a t-test for two groups or using Bartlett’s test followed by Dunnett’s comparison for three groups. Differences were considered significant when the p value was <0.05.
Koga Y., Meguro H., Fujieda H., Ueno Y., Miwa K, & Kainoh M. (2019). A new hydrophilic polysulfone hemodialysis membrane can prevent platelet–neutrophil interactions and successive neutrophil activation. The International Journal of Artificial Organs, 42(4), 175-181.
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6

Analyzing Inpatient and Outpatient Differences

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The Wilcoxon rank sum test was used to test for differences between inpatient and outpatient continuous variables, and chi square or Fisher's exact test was used to compare distributions of percentages of categorical variables between inpatient and outpatient visits. The significance level was set at 0.05. Data were analyzed using SAS software version 9.4 of the SAS system for Windows copyright © 2002–2012 SAS Institute Inc. Cary, NC, USA.
von Gaudecker J.R., Oduor C., Ofner S., Oyungu E., Said J, & Buelow J. (2020). Pattern and frequency of neurological and neurosurgical care of adult inpatients and outpatients at a tertiary referral hospital in Kenya. eNeurologicalSci, 21, 100286.
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7

Statistical Analysis of Experimental Data

Cited 2 times
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Data are expressed as mean ± standard error of the mean (SEM). Significant differences between groups were analyzed using one-way analysis of variance (ANOVA), followed by the Student’s t-test, as previously reported [20 (link),21 (link)]. Probabilities less than 5% (P < 0.05) were considered significant. All statistical analyses were performed using the SAS system (SAS Institute Japan, Tokyo, Japan).
Watanabe Y., Nakayama K., Taniuchi N., Horai Y., Kuriyama C., Ueta K., Arakawa K., Senbonmatsu T, & Shiotani M. (2015). Beneficial Effects of Canagliflozin in Combination with Pioglitazone on Insulin Sensitivity in Rodent Models of Obese Type 2 Diabetes. PLoS ONE, 10(1), e0116851.
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8

Phase 1a and 1b Study Analysis

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Because of the nature of the Phase 1a and Phase 1b studies, statistical analyses were descriptive only. In each study, data for all subjects randomized to placebo were pooled to create the placebo treatment group. Categorical data were summarized in tables listing the frequency and the percentage of subjects in each treatment group. Continuous data were summarized in tables listing the mean, standard deviation, median, minimum, and maximum in each treatment group. All statistical computations were performed using the SAS® system.
Halstenson C.E., Shamp T., Gargano M.A., Walsh R.M, & Patchen M.L. (2016). Two randomized, double-blind, placebo-controlled, dose-escalation phase 1 studies evaluating BTH1677, a 1, 3–1,6 beta glucan pathogen associated molecular pattern, in healthy volunteer subjects. Investigational New Drugs, 34, 202-215.
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9

Statistical Analysis of Experimental Protocols

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The assumptions of equality of variances and normal distribution were checked for all
variables (Shapiro-Wilk test). Intragroup comparisons were analyzed using Student's
paired t-test or Wilcoxon's signed rank test, one-way repeated measures ANOVA or the
Friedman repeated measures with the Student-Newman-Keuls method for
post-hoc analysis. To identify intergroup differences, one-way
ANOVA or the Kruskal-Wallis test was used, with Dunn's Method as the
post-hoc analysis. Moreover, analysis of variance was applied
based on a generalized linear mixed model for two factors: group (fixed) and time of
evaluation as repeated measures. This analysis used the t-test adjusted with the
Tukey-Kramer test. Pearson's correlation was applied and SigmaPlot (Systat Software,
San Jose, CA, USA) was used. A generalized linear mixed model was developed with SAS
System (SAS Institute Inc, release 9.3. SAS Institute Inc., Cary, NC, USA; 2010).
Aranha M.F., Müller C.E, & Gavião M.B. (2015). Pain intensity and cervical range of motion in women with myofascial pain treated with acupuncture and electroacupuncture: a double-blinded, randomized clinical trial. Brazilian Journal of Physical Therapy, 19(1), 34-43.
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10

Comprehensive Data Analysis Approach

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Data analysis was performed using Version 9.4 of the SAS System (SAS Institute Inc., Cary, NC). For descriptive statistics of the baseline and follow-up data, mean with standard deviations or median (range) were calculated for continuous data and proportions (%) for categorical data. Between group comparisons were conducted by the student's t-test (or the Wilcoxon rank sum, if the distribution was not symmetric) for continuous variables and the chi-square test for categorical variables. Analyses were performed on subsets of patients with test results available at specific time intervals, and expressed as the number with response versus the number tested at each time point. Kaplan-Meier (KM) analysis was used to plot the cumulative probability of responses over time. Data analysis was performed for the entire cohort and also separately for the subgroup of patients recruited at community-based centers and those recruited at university-based centers. Statistical significance was assessed at the 0.05 level.
Ahn J., Lee H.M., Lim J.K., Pan C.Q., Nguyen M.H., Kim W.R., Mannalithara A., Trinh H., Chu D., Tran T., Min A., Do S., Te H., Reddy K.R, & Lok A.S. (2015). Entecavir Safety and Effectiveness in a National Cohort of Treatment-Naïve Chronic Hepatitis B Patients in the US - the ENUMERATE study. Alimentary pharmacology & therapeutics, 43(1), 134-144.
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