The largest database of trusted experimental protocols

5 311 protocols using sas v9

1

Evaluating Sow Vulva Scores and Litter Performance

Check if the same lab product or an alternative is used in the 5 most similar protocols
Statistical Analysis Systems University Edition, version 9.4 (Cary, NC) was used for all statistical analysis. Regression analyses (PROC REG, SAS v.9.4, SAS Inst. Inc., Cary, NC) were completed to evaluate the relationships between BW and VW measures and to generate coefficient of determination values. Group means for each fixed effect level were compared using PROC TTEST. A chi-square (χ 2) analysis was performed (PROC FREQ, SAS v.9.4) to estimate the association between vulva score classification and ability to achieve P1 and P2. Additionally, for each vulva scoring method (VSA, VSB, or FS) mixed model methods (PROC MIXED, SAS v.9.4) were used to analyze the litter performance data, with a model where the fixed effects were: vulva score, sow farm, birth week, and the associated interactions. The random error term was the only random effect included in any model used for analyses. Prior to analyzing litter performance data, data points extending beyond 2.5 SDs from the mean for TB, BA, SB, and MM were considered outliers and were removed from analysis. The number of outliers from any of the analyses ranged from 0 to 6 animals.
+ Open protocol
+ Expand
2

ETDQ-7 and PEq5 Diagnostic Accuracy

Check if the same lab product or an alternative is used in the 5 most similar protocols
Sensitivity and specificity for Groups 1 and 2 assignment were computed using the ETDQ-7 score of ≥ 14.516 (link) and PEq5<60% as an indication of ETD. The ETDQ-7 does not discriminate if symptoms come from left, right or both ears, so to avoid assumptions that could create a selection bias, the primary PEq5 statistical analysis was done at participant level using the lowest PEq5 for two ears, and at ear level using the same ETDQ-7 for both ears.
For participant level analysis, ETDQ-7 and PEq5 scores were compared between symptomatic and control groups using two-sample Wilcoxon test (proc npar1way, SAS v.9.4, Cary, NC). Exact 95% Clopper-Pearson confidence intervals (CI) were obtained for sensitivity and specificity (proc freq, SAS, v. 9.4) and Fisher exact test was used for comparing sensitivity or specificity between different subgroups of patients.
Kendall’s correlation coefficient was used to test association between the PEq5 and ETDQ-7 scores (proc corr, SAS, v.9.4). Association of ETDQ-7 scores with the inadequate PEq5<60% as well as with group assignment were assessed using empirical Area under the ROC curves (AUC).23 (link),24 At ear-level data, statistical analysis (including evaluating Kendall’s coefficient and AUC) was performed using non-parametric bootstrap CI for clustered data with participant as a resampling unit, based on 10,000 bootstrap samples.25
+ Open protocol
+ Expand
3

Pig Growth Performance Evaluation

Check if the same lab product or an alternative is used in the 5 most similar protocols
Each pig was considered as the experimental unit for all data analyses. Residuals were tested for normality using the Shapiro–Wilk test and by examining the normal probability plot using the UNIVARIATE procedure of SAS v9.4 [25 ]. Two different analyses were performed: Model 1 included data from 16 days post-weaning up to 20 weeks of age, when the first group of pigs reached 110 kg of BW and were sent to slaughter. Predicted variables included BW, ADG, FI, ADFI and FCR. Mixed model equation methods accounting for repeated measurements were used in PROC MIXED of SAS v9.4 [25 ]. Model 2 included data from 15 days post-weaning until all pigs reached target slaughter weight, and the same predicted variables were investigated as per Model 1 plus DTSW. Data were also analyzed using mixed model equation methods in PROC MIXED of SAS v9.4 [25 ]. For both analyses, models included birth and weaning body weight classification, observation day and their interaction, and sex as fixed effects. Pig was included as a random effect. Multiple means comparisons were done using Tukey–Kramer’s correction. Results for the fixed effects are reported as least square means ± standard error. Alpha level for determination of significance and trends were 0.05 and 0.10, respectively.
+ Open protocol
+ Expand
4

Variance Components and GWAS Analysis

Check if the same lab product or an alternative is used in the 5 most similar protocols
Analysis of variance was performed using the MIXED procedure in SAS v9.4 (SAS Institute, 2013 ) according to this model: Trait = µ + Genotype + Year + Genotype-by-Year + rep(Year) + Error. Pearson’s correlation analysis among traits was conducted using the CORR procedure in SAS v9.4 (SAS Institute, 2013 ). Variance components for computing broad-sense heritability (H2) were generated using the VARCOMP procedure in SAS v9.4 using restricted maximum likelihood estimation (REML) with all effects in the model treated as random (SAS Institute, 2013 ). The broad-sense heritability on an entry-mean basis was calculated following the formula provided by Holland et al., 2003 (link). Trait data were converted to best linear unbiased estimates (BLUEs) in SAS v9.4, which were then used to conduct GWAS.
+ Open protocol
+ Expand
5

Genomic Regions Associated with ROH

Check if the same lab product or an alternative is used in the 5 most similar protocols
To identify the genomic regions most commonly associated with ROH for the meta-population and for groups on the basis of production purposes (mutton, wool and pelt and dual purpose breeds), Golden Helix SVS was used to analyse the incidence of common runs per SNP, which was then plotted against the position of the SNP along the chromosome (OAR).
ROH islands were defined as clusters of runs that were > 1000 Kb with a minimum of 30 SNPs and found in more than 20 samples and analysed using Golden Helix SVS. For each sample, the proportion of SNPs in the ROH island was estimated. The mean proportion of SNPs per sample per ROH islands was determined using Proc MEANS procedure in SAS v9.4 [25 ]. The variance in mean proportion of SNPs in ROH islands amongst breeds was analysed using the Proc GLM in SAS v9.4 [25 ] using the following model:
Proportion of SNPs per ROH island = μ + Bi + e.where:
μ = overall mean;Bi =Breed effect and;e = random residual error
+ Open protocol
+ Expand
6

Comprehensive Multi-Omics Data Analysis

Check if the same lab product or an alternative is used in the 5 most similar protocols
In general, principal component analysis used all samples including outliers and was performed using SAS v9.4 PROC PRINCOMP (no further options). The analysis was conducted on log-transformed signal, centered by gene.
Hierarchical clustering in general used correlation as a measure of similarity and centroid-based linkage.
Simple differential analysis of groups using the RNA-Seq data was performed using two methods. In one method, the well-known t-statistic with a mildly stringent unadjusted p-value of 0.001 was combined with a fold change threshold of 1.5 to generate comparator lists. In a comparator method, we used the RNA-Seq differential method DESeq228 (link) with the same fold change threshold and a (multiple testing) corrected or adjusted p-value of 0.01. The linear model for the RNA-Seq analysis utilized 148 of the original 150 samples (specimens 4-4 and 51-4 omitted). The linear model was performed using SAS v9.4 PROC MIXED with subject as a random effect and terms for fixed effects of tissue type, collection site, and preservation protocol. Only effects with unadjusted p < 0.001 were kept for meta-analysis across genes.
We used Levene’s test (two-sided) for homogeneity of variance (SAS) when examining variation in miRNA expression by protocol.
+ Open protocol
+ Expand
7

Comparative Tubomanometry Curve Analysis

Check if the same lab product or an alternative is used in the 5 most similar protocols
Individual characteristics of the tubomanometry curves were compared between the two groups. Statistical analysis was conducted using mixed models (proc mixed, SAS v.9.4) for the normalized values of individual characteristics (based on Box‐Cox transformation; proc transreg, SAS v.9.4). Mixed models accounted for correlation between observations obtained for the same participants using different ears (left and right) as well as under the different test pressure levels (30, 40, and 50 mbar). Secondary analyses verified consistency of the findings by adjusting for age and sex of participants. Age, sex and race compositions of the two groups were compared using exact nonparametric tests.
+ Open protocol
+ Expand
8

Multivariate Analysis of Low Back Pain

Check if the same lab product or an alternative is used in the 5 most similar protocols
The analyses were performed using the SAS statistical software for Windows (SAS V.9.4; SAS Institute). Associations were modelled using the general linear model (Proc GLM, SAS V.9.4) controlling for confounders and weighted based on information from high-quality national registers at Statistics Denmark, which included gender, age, occupation, highest completed education, family income, family type and origin.25 (link) The residuals of the outcome variable (LBP) were normally distributed through visual inspection. Because a strong correlation exists between a 0–10 NRS and a 0–10 Visual Analogue Scale,36 (link) the 0–10 (11 point) ordinal NRS in this study is treated as a continuous scale. Estimates are reported as least square means pain intensity (NRS) and 95% CIs and between-group least square means differences and 95% CI. An alpha level of <0.05 was chosen as statistically significant differences.
+ Open protocol
+ Expand
9

Extrusion Process Effects on Starch Properties

Check if the same lab product or an alternative is used in the 5 most similar protocols
The experiment was conducted as a complete randomized design (CRD). Single degree of freedom orthogonal contrasts for extrusion outputs, starch analyses, and viscosity (measured by RVA) were performed using the generalized linear mixed model (GLIMMIX) procedure from statistical analysis software (SAS v 9.4; Cary, NC, USA), and linear (L) and quadratic (Q) relationships were considered significant at a p < 0.05. Analysis of variance of kibble measurements and expansion indices were performed by the GLIMMIX procedure (SAS v 9.4; Cary, NC, USA) with replicate nested within the diet, and the means were considered significantly different at a p < 0.05. Multiple testing was adjusted by the Tukey–Kramer post hoc test.
+ Open protocol
+ Expand
10

Spatial Empirical Bayes Smoothing for Rural MI Mortality

Check if the same lab product or an alternative is used in the 5 most similar protocols
MI mortality risks per 100,000 population were calculated and directly age-standardized to the 2000 US Standard Population [15 ] in SAS v.9.4 (SAS Institute; Cary, NC). Despite pooling death counts by three-year intervals to address the small number problem, a number of rural counties still had < 25 MI-deaths. According to Curtin and Klien [16 ], such areas are considered small areas; hence, unsmoothed age-adjusted risks from these areas would be highly unstable due to high variances. Therefore, to minimize the impact of the high variances and adjust for spatial autocorrelation (i.e. clustering), we computed Spatial Empirical Bayes (SEB) smoothed risks using 1st order queen weights in GeoDa [17 (link)]. All descriptive analyses were done in SAS v.9.4 (SAS Institute; Cary, NC).
+ Open protocol
+ Expand

About PubCompare

Our mission is to provide scientists with the largest repository of trustworthy protocols and intelligent analytical tools, thereby offering them extensive information to design robust protocols aimed at minimizing the risk of failures.

We believe that the most crucial aspect is to grant scientists access to a wide range of reliable sources and new useful tools that surpass human capabilities.

However, we trust in allowing scientists to determine how to construct their own protocols based on this information, as they are the experts in their field.

Ready to get started?

Sign up for free.
Registration takes 20 seconds.
Available from any computer
No download required

Sign up now

Revolutionizing how scientists
search and build protocols!