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Enterprise guide 7

Manufactured by SAS Institute
Sourced in United States
About the product

Enterprise Guide 7.1 is a data analysis software application developed by SAS Institute. It provides a graphical user interface for accessing and managing SAS data and analytics. The core function of Enterprise Guide 7.1 is to facilitate data manipulation, analysis, and visualization tasks through a user-friendly, point-and-click environment.

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27 protocols using «enterprise guide 7»

1

Evaluating Rumen Fermentation Dynamics

2024
All data from the in vitro experiments were analyzed using the GLM procedure in SAS (Enterprise Guide 7.1, SAS Institute Inc., Cary, NC, USA). The incubation time and RP treatments were assigned to the statistical model, and fixed effects were considered. Differences between the treatment means were analyzed using Tukey’s multiple comparison test. In the in vitro experiment, orthogonal polynomials were used to test the linear, quadratic, and cubic effects of RP addition on changes in ruminal variables. All data are presented as least-squares means, and significance was declared at p < 0.05.
All data from the in vivo experiments were analyzed using the GLIMMIX procedure of SAS (Enterprise Guide 7.1, SAS Institute Inc., USA). Treatment, period, and steer were assigned to a statistical model. Treatment was considered a fixed effect, and period and steer were considered random effects. Differences between treatment means were analyzed using Tukey’s multiple comparison test. All data are presented as least-squares means. Significance was declared at p < 0.05 and tendency was determined at 0.05 ≤ p < 0.10.
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2

Clinical Outcomes Analysis Protocol

2024
Qualitative variables are described as frequency and percentage and quantitative variables as mean and SD. Comparisons were made using χ2 tests for categorical variables and the Student t‐test for continuous variables. The analysis for clinical outcomes during the whole follow‐up in the groups of interests was performed using the Mantel–Haenszel method to estimate standardized incidence rates and incidence rate ratios (IRRs) with 95% confidence intervals (CIs). A competing risk analysis was performed using the Fine and Gray model with subdistribution hazard ratios (SHRs) used to denote the risk of a particular outcome. The competing risk included noncardiovascular death for cardiovascular death, and all‐cause death for nonlethal clinical outcomes. There was no competing risk analysis for all‐cause death. All comparisons with p < 0.05 were considered statistically significant. All analyses were performed using Enterprise Guide 7.1, (SAS Institute, Cary, NC, USA).
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3

Analyzing Temporomandibular Joint Structures

2024
After the investigations, the data were entered into a suitable program (Microsoft Excel, Version 2309, 2023) and then analysed statistically (SAS, Enterprise Guide 7.15 and SAS software, version 9.4). First, a descriptive evaluation of the data was carried out to ensure a percentage representation of the apparent structures. This was followed by the Fisher test, which was used to check for differences in the lateral comparison between the right and left sides of the TMJ. In addition, a Cohen’s kappa was calculated along with a 95% confidence interval to determine agreement between the two observers. A p-value of <0.05 was considered significant.
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4

Temporomandibular Joint Morphometrics

2024
All determined values were entered into a suitable software [Microsoft Excel (Version 2,309) 2023]. Subsequently, the extracted data were transferred to SAS software (Enterprise Guide 7.15 and SAS software 9.4) and to the GraphPad Prism analysis program to be evaluated. For the graphic representation of the won values, the program GraphPad Prism was used likewise. An intra-observer comparison was performed using the intraclass correlation coefficient (ICC) to identify measurement deviations of the determined three measurements per variable. Following this, the arithmetic mean was formed from these three measurements, which was used for the statistical evaluation. Thereafter, a skull index-oriented data analysis as well as a weight classified data analysis was performed to investigate a possible correlation of the respective parameter with the determined TMJ values. In addition, an evaluation of the relationship between the width and depth of the Fossa mandibularis was carried out. For this purpose, the two values were divided with each other, and the quotient determined was used for the evaluation. A gender-specific analysis was also performed.
A descriptive data analysis was conducted. The Shapiro–Wilk test was then used to test the normal distribution. A simple ANOVA was carried out to determine the significance of the data. As the data in each data set was not always normally distributed, different post-hoc tests were undertaken depending on the distribution pattern. For non-normally distributed data sets, the Kruskal-Wallis test was performed, followed by Dunn’s multiple comparison test. If the data was normally distributed, the Tukey multiple comparison test was used. Since the data sets were normally distributed for the evaluation of possible measurement discrepancies between the left and right temporomandibular joint of the animals, a t-test was performed. A p-value of <0.05 was considered significant.
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5

Persistence of Patiromer Treatment

2023
For non-normal baseline characteristics and changes in K+ values, the median and interquartile range were also calculated. Sample paired t tests were used to calculate differences in baseline and FU K+ concentrations. Kaplan–Meier curves were used to describe the duration of patiromer treatment courses (i.e., persistence). Data extraction, processing, and management were conducted using Microsoft SQL Server Management Studio 17.4 (Microsoft Corporation, Redmond, WA). Frequency and percentages were computed using SAS 9.4 (SAS Institute, Cary, NC) and Enterprise Guide 7.1 (SAS Institute, Cary, NC).
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Top 5 protocols citing «enterprise guide 7»

1

Plasma Albumin Predicts Type 2 Diabetes

Statistical analyses were performed using SAS software (SAS Version 9.4 or Enterprise Guide 7.13; SAS Institute, Cary, North Carolina). Non-normally distributed data were log transformed (e.g. M and AIR). Pearson or Spearman correlation analysis was used to quantify the relationships between plasma albumin and variables of interest before and after adjustment for covariates. For comparison of participant characteristics, unpaired t-test, Wilcoxon rank-sum, and Chi squared tests were used where appropriate. To assess albumin as a predictor of incident T2D, a Cox model was used to calculate hazard ratios for development of diabetes, adjusting for baseline age, sex, %fat, heritage, M, AIR, and fasting and 2 h-PG concentrations. All analyses used only baseline measurements because our primary interest was the clinically-relevant predictive value of plasma albumin at a single time-point for subsequent development of T2D. Proportional hazards assumptions were checked by assessment of plots of log[− log(survival)] versus log of survival time and inclusion of a time-dependent interaction term. The follow-up time was truncated to 15 years to satisfy the proportionality assumption. To facilitate comparisons, continuous variables including were standardized (i.e. mean = 0 and SD = 1) and the hazard ratio was reported per SD. Cumulative incidence of diabetes was estimated from the Kaplan–Meier method for participants above and below the median. The association between plasma albumin and residual adipose tissue expression was performed, adjusting for potential confounders (e.g. age, sex, %fat), using linear regression. An alpha level of 0.05 was chosen for analyses.
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2

Adiposity-Dependent Fluid Regulation

Statistical analyses were performed using SAS software (SAS Version 9.4 or Enterprise Guide 7.13; SAS Institute, Cary, North Carolina). Non-normally distributed data were log transformed. For comparison of participant characteristics, unpaired t-test, Wilcoxon rank sum test, Chi-squared test, and Fisher’s exact tests were used where appropriate. An unpaired t-test was used to test for differences between the lean group and the group with obesity with respect to (a) water intake after both dehydrating interventions and (b) hormone concentrations after the 24-h water deprivation (immediately after the fasting period and after rehydration).
For the hypertonic saline infusion, mixed models (PROC MIXED) were used to analyze repeated measures of thirst, serum sodium, serum osmolality, change in plasma volume, and hormones (copeptin, leptin, angiotensin II, aldosterone, ANP, BNP, and apelin) using first-order autoregressive (1 (link)) covariance structure. Since leptin differed substantially by adiposity groups as expected, mixed models of copeptin response to hypertonic saline were stratified by adiposity groups to model the relationship between leptin and copeptin. For urine osmolality and urine sodium concentration after hypertonic saline infusion, a general linear model was used to evaluate for differences between the lean group and the group with obesity adjusting for baseline values.
For the 24-h water deprivation, mixed models (PROC MIXED) were used to analyze repeated measures of thirst using unstructured covariance structure for the water deprivation intervention. AIC was used to select the appropriate covariance matrices. All models were adjusted for baseline thirst scores.
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3

Maladaptive Eating Behaviors and Dehydration

Statistical analyses were performed using SAS software (SAS Version 9.4 or Enterprise Guide 7.13; SAS Institute, Cary, North Carolina, USA). Non-normally distributed data were log transformed. For comparison of participant characteristics, unpaired t-test, Wilcoxon rank sum test, Chi-squared test, and Fisher’s exact tests were used where appropriate.
Mixed models were used to analyze repeated measures of thirst using first-order autoregressive AR(1) covariance structure for the hypertonic saline infusion and unstructured covariance for the 24-h water deprivation. AIC was used to select the appropriate covariance matrices. All models were adjusted for baseline thirst scores. In the mixed models, interaction term for eating behavior constructs and adiposity group were also assessed. Models with statistically significant interaction terms were further stratified by adiposity group. To assess the association between maladaptive eating behaviors and ad libitum water intake after each dehydrating condition, general linear models were used, adjusting for adiposity group. General linear models were also used to assess whether thirst (defined as the last VAS thirst score immediately prior to ad libitum water intake) predicted water intake, adjusting for adiposity group.
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4

COVID-19 Convalescent Plasma Transfusion Protocol

This study was approved by the University of Wisconsin Institutional Review Board. Cases met all criteria for enrollment under the Mayo Clinic Expanded Access Protocol (IND # 20-003312) and gave written, informed consent for CP transfusion and data collection. Enrollment criteria are described elsewhere. (11 ) Briefly, all enrollees had laboratory confirmed COVID-19 with either severe or life-threatening disease. Severe disease was defined as the presence of subjective dyspnea, a respiratory frequency ≥ 30/min, blood oxygen saturation ≤ 93% on room air, partial pressure of arterial oxygen to fraction of inspired oxygen ratio <300, and/or lung infiltrates > 50% within 24 to 48 hours. Life-threatening disease was defined as respiratory failure, septic shock, and/or multiple organ dysfunction or failure at the time of transfusion.
COVID-19 convalescent plasma was collected from a local donor recruitment and referral program in collaboration with the American Red Cross. Briefly, in response to guidance from the FDA dated April 3, 2020 We convened a local working group to establish a University of Wisconsin Hospital-based COVID-19 Convalescent Plasma program for both candidate recipients and potential COVID-19 recovered donors. Stakeholders were assigned to issues within their expertise including transfusion, the University’s Office of Clinical Trials, the Media Relations, and the local American Red Cross donor center. The Transfusion Medicine section developed an inventory and ordering process for convalescent plasma units within our electronic medical record system. The Office of Clinical Trials worked closely with American Red Cross and Food and Drug Administration to ensure compliance with rapidly evolving rules. Office of Clinical Trials staff also worked with potential donors identified by University of Wisconsin clinician referral or self-referral through local media coverage and via information provided to patients in the discharge instructions after all COVID-19 related hospital admissions. Potential donors were then screened and recruited to donate via a scripted telephone interview. The local American Red Cross established a process for receiving prospective donors and worked with national leadership to develop long-term protocols.
Recipient data were abstracted from the medical record into a standardized case report form. Results were presented with descriptive statistics. Parametric and non-parametric tests were used as appropriate. All analyses were performed using commercially available statistical software (Enterprise Guide 7.1, SAS, Cary, North Carolina).
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5

Propensity-Matched Comparison of TAVR Valves

Qualitative variables are described as frequency and percentages and quantitative variable as means (SDs). Comparisons were made using χ 2 tests for categorical variables and the Student t test or nonparametric Kruskal-Wallis test, as appropriate, for continuous variables.
Because of the nonrandomized nature of the study, and considering the SDs in baseline characteristics and the year of implantation, propensity score matching was used to control for potential confounders of the treatment outcome relationship. Propensity scores were calculated using logistic regression with valve type as the dependent variable. The propensity score included 38 variables, including baseline characteristics (most of which are listed in Table 1), year of implantation, because the Sapien 3 BE valve was available a few months earlier than the Evolut R SE valve, and hospital procedural volume for TAVR by quartile (full list of variables in the online-only Data Supplement). For each patient with a BE valve, a propensity score-matched patient with an SE valve was selected (1:1) by using the one-to-one nearest neighbor method (with a caliper of 0.001 of the SD of the propensity score on the logit scale) and no replacement. We assessed the distributions of demographic data and comorbidities in the BE and SE valve cohorts with standardized mean differences, which were calculated as the difference in the means or proportions of a variable divided by a pooled estimate of the SD of that variable. A standardized mean difference of ≤5% indicated a negligible difference between the means of the 2 cohorts.
For the outcomes analysis in the matched cohort, the incidence rates (%/y) for each outcome of interest during followup were estimated in BE and SE groups and compared by using incidence rate ratios. The corresponding asymptotic 2-sided 95% CI of the relative risk was reported. A logistic regression model was used for the specific outcomes of pacemaker implantation at 30 days and cardiovascular death. Hazard ratio and odds ratio (OR) were reported. P values are reported without and with correction for multiple comparisons using Bonferroni correction. All comparisons with P<0.05 were considered statistically significant. All analyses were performed using Enterprise Guide 7.1 (SAS Institute Inc) and STATA version 12.0 (Stata Corp).
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