A univariate regression analysis was conducted to assess if there were any correlations between neuroimaging data and abnormal CM. For volumetric measurements, post-conceptional age was used as a covariate. In order to test for the effect of surgical timing on neuroimaging variables, a mixed-effect model was used. Inter-reader reliability for conventional MRI findings was assessed by calculating the Cohen’s kappa coefficients between readers for each category. Post hoc analysis using False Discovery Rate (FDR) was conducted to minimize Family Wise Error rates, with FDR corrected p-values of <0.05 considered statistically significant. All analyses were performed using SAS 9.3 (SAS Institute Inc. 2010. SAS/STAT ® 9.3).
Sas stat 9
Manufactured by SAS Institute
Sourced in United States
SAS/STAT 9.4 is a statistical software package that provides a wide range of data analysis and modeling capabilities. It includes tools for regression, analysis of variance, multivariate analysis, survival analysis, and more. SAS/STAT 9.4 is designed to help users analyze data, build predictive models, and make informed decisions.
Automatically generated - may contain errors
Lab products found in correlation
Stata 12, by StataCorp (6 mentions)
Sas 9, by SAS Institute (6 mentions)
Sigmaplot 11, by Grafiti LLC (3 mentions)
Odyssey imaging software, by LI COR (2 mentions)
Prism 6.02 for windows, by GraphPad (2 mentions)
Stata 14, by StataCorp (2 mentions)
Stata 15, by StataCorp (1 mentions)
Stata 16, by StataCorp (1 mentions)
Sigmaplot 12, by Grafiti LLC (1 mentions)
Statistica version 10, by StatSoft (1 mentions)
Prism 9, by GraphPad (1 mentions)
Spss 11.0 for windows, by IBM (1 mentions)
Human mcp1 elisa kit, by Thermo Fisher Scientific (1 mentions)
Human il 6 immunoassay, by R&D Systems (1 mentions)
Spss v20, by IBM (1 mentions)
Spss ver 20, by IBM (1 mentions)
82 protocols using sas stat 9
1
Correlating Neuroimaging and Congenital Malformations
2
Predicting Maize Cultivar Nutritional Profiles
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The data were analysed using the PROC GLM procedure of SAS/STAT® 9.3 (Statistical Analysis System). The model included maize cultivar (n = 13), morphological fractions (leaves and stems) and their interactions as fixed effects, and a week effect. The latter was not significant and removed from the model. The in vitro gas production parameters of one sample were taken as the mean of the parameters obtained from the two runs of each sample. The repetition of the in vitro gas production parameters and chemical composition was two, as there were two samples for each cultivar (described in Section 2.3 In vitro gas production). Differences among main effects were analysed using the Tukey–Kramer's multiple comparison procedure. Regression equations were derived to predict A2, B2 and cumulative gas production at 72 hr (GP72) from each chemical component. Furthermore, variables were selected using the stepwise selection method (PROC REG procedure of SAS 9.3, 2011) with p ≤ .05 as the significance level for the variables to enter or stay in the model. Variables considered for addition or subtraction in the stepwise approach included cellulose, hemicellulose, ADL, CP and rest fraction content.
3
Analyzing Genotype and Treatment Effects
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Data are presented as mean ± SEM. We used two-way analysis of variance (ANOVA) based on the factors treatment (GFP-AAV or IFNγ-AAV injection) and survival time (4 or 6 months) to evaluate main effects and treatment-by-time interaction within genotype (WT and APP/PS1). No statistical comparisons were made across genotype. Because treatment effects at each time point were of interest a priori, data are presented by treatment and time point whether or not the treatment-by-time interaction is significant.
Due to a high proportion of zero values, we used nonparametric two-way ANOVA on ranks to evaluate distributional differences in ELISA-determined IFNγ protein levels. Similarly, we used two-way ANOVA on ranks to evaluate differences in gene expression (measured by fold change from GFP) as these data did not meet parametric ANOVA assumptions. Ranks were assigned using the RANK procedure, and ANOVA was performed using the GLM procedure in SAS/STAT 9.3® (SAS, Cary, NC, USA). Statistical significance was set at P < 0.05.
Due to a high proportion of zero values, we used nonparametric two-way ANOVA on ranks to evaluate distributional differences in ELISA-determined IFNγ protein levels. Similarly, we used two-way ANOVA on ranks to evaluate differences in gene expression (measured by fold change from GFP) as these data did not meet parametric ANOVA assumptions. Ranks were assigned using the RANK procedure, and ANOVA was performed using the GLM procedure in SAS/STAT 9.3® (SAS, Cary, NC, USA). Statistical significance was set at P < 0.05.
4
Factorial Analysis of Ketamine and UCO
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The experiment involved four groups of animals (±ketamine, ±UCO) in a full factorial randomized complete design. Data were analyzed by three‐way ANOVA, with stimulus (±UCO), treatment (±ketamine), and time (repeated measurements) as factors. We used the Mixed Procedure of SAS/STAT 9.3® (SAS Institute Inc., Cary, NC), and determined statistical differences with the Duncan post hoc test. Data are presented as mean ± standard error of the mean (SEM), and significance was declared at P < 0.05.
5
Statin Use and Pneumonia Risk in MI
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The primary analysis was to examine the association between statin use and the risk of pneumonia requiring hospitalization. To investigate the effect of statin pretreatment before an MI event, the secondary analysis was limited to MI patients unexposed to statin pretreatment. The stratified analysis of the patients with a specific characteristic was also performed. For all variables of interest, risk estimates were computed as both univariate and multivariate analyses, with additional adjustments for potential confounders. Conditional logistic regression was used to estimate unadjusted and adjusted odds ratios (ORs) and 95 % confidence intervals (CIs) for the association of statin exposure and the risk of hospitalization for pneumonia; and the incidence density sampling yields ORs that are interpretable as unbiased estimates of the incidence ORs [18 (link), 19 (link)]. All analyses were performed using SAS/STAT 9.3 (SAS Institute Inc., Cary, NC, USA) and STATA 12 (Stata Corp LP, College Station, TX, USA); P < 0.05 was considered significant.
6
LA Supplementation Effect on Analyses
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All statistical analyses were carried out using SAS/STAT® 9.3 software (Copyright © 2011, SAS Institute Inc., Cary, NC, USA). Data were performed to one-way ANOVA with treatment effect using the General Linear Model (GLM) procedure. The main factor was LA supplementation levels, and the means were compared for significance by Duncan’s multiple-range tests. A p-value of less than 0.05 was considered significant.
7
Comparing Neuroinflammation in Neurodegenerative Disorders
8
Biofilm Disruption Assessment Protocol
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Mean and standard deviation were calculated for each strain in both growth environments (TSBg and PCs). A mixed model analysis was performed to test the difference between treated and untreated biofilms with proteinase K or DNase I. In the model, replication ID was fitted as random effects to control the potential clustering effect. Data from the two study environments were analysed separately. A p-value of <0.05 was considered statistically significant. All the analyses were performed in SAS software (SAS Institute Inc., SAS/STAT 9.3).
9
Ovine Gene Expression in Hypoxia
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Data are presented as mean values ± standard error of the mean with consideration for statistical significance at P < 0.05. The ovine Agilent 15.5 K array results were analyzed with Bioconductor's Limma package for R software v.2.15.1 (Smyth 2006 ), employing moderated t test using empirical Bayes method for small sample size per group (P < 0.05). Pairwise comparisons of real‐time PCR data were analyzed using Student's t test, with P < 0.05 considered statistically significant.
For the histological analysis, the experiment involved a 2 × 2 factorial completely randomized design with stimulus (hypoxia, normoxia) and treatment (control, ketamine) as factors. A generalized linear model with Poisson distribution for log count data observations was used. Significance was declared at P < 0.05, and if detected, post hoc mean comparison with Bonferroni correction was performed. Least square means and their corresponding standard errors are expressed in the original scale. Statistical analysis was conducted using the Genmod Procedure of SAS/STAT® 9.3 (SAS Institute Inc., Cary, NC).
For the histological analysis, the experiment involved a 2 × 2 factorial completely randomized design with stimulus (hypoxia, normoxia) and treatment (control, ketamine) as factors. A generalized linear model with Poisson distribution for log count data observations was used. Significance was declared at P < 0.05, and if detected, post hoc mean comparison with Bonferroni correction was performed. Least square means and their corresponding standard errors are expressed in the original scale. Statistical analysis was conducted using the Genmod Procedure of SAS/STAT® 9.3 (SAS Institute Inc., Cary, NC).
10
Analysis of Biomarker Responses
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We used the Mixed or NPAR1WAY Procedure of SAS/STAT 9.3® (SAS Institute Inc., Cary, NC, USA) for data analysis. For blood gas, endocrine, western blot, and ELISA data, the statistical analysis involved a one-way ANOVA with repeated measures (baseline, 1 h, and 5 h post exposure). Appropriate covariance structures were used by selecting the best fit statistics. Cell culture data RT-qPCR was analyzed as one-way ANOVA with four treatments (hepatocytes ± LPS, hepatic macrophages ± LPS). Differences between means were obtained using the Fisher’s least significant difference test. Hepatic WB and RT-qPCR data values failed to follow the normal distribution and thus were analyzed by the Mann–Whitney U nonparametric test. Significance was declared at P < 0.05.
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