Sas statistical package v 9

Manufactured by SAS Institute

Sourced in United States

The SAS statistical package v. 9.4 is a comprehensive software solution for data analysis, statistical modeling, and reporting. It provides a wide range of analytical capabilities, including data management, visualization, and advanced statistical techniques. The software is designed to handle large and complex data sets, enabling users to gain valuable insights and make informed decisions.

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SAS 9.4 (20063 citations) SAS statistical package (348 citations) SAS package (54 citations) SAS package 9.4 (15 citations) SAS statistical package 9.4 (13 citations) SAS Statistical Software Package v.9.2 (6 citations) SAS package v 9.4 (3 citations) SAS V.9.4 Statistical Package (2 citations) SAS v.9.1 statistical package (2 citations) SAS V.9.3 statistical package (2 citations)

31 protocols using sas statistical package v 9

Comparison of Milk Composition and Cheese Quality

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SAS statistical package v 9.4 (SAS Institute Inc., Cary, NC, USA) was used for statistical analysis (2019). The SCC in the milk was subjected to logarithmic transformation before statistical verification [20 (link)].
The composition of whole milk in summer and winter season was compared with Student’s t-test.
In order to determine the significance of the experimental factors (season and type of milk) and the quality of the milk, physicochemical, rheological and organoleptic traits of cheese, a two-way analysis of variance was performed (GLM-SAS procedure v. 9.4 2019, SAS Institute Inc., Cary, NC, USA) according to the following linear model:
y_ijk = µ + s_i + c_j + (sc)_ij + e_ijk, where y_ijk is the phenotypic value of the trait; µ is the overall mean, s_i is the fixed effect of the i-th season of milk collection (i = 1, 2); c_j is the fixed effect of the j-th type of milk (j = 1, 2); (sc)_ij is s x c interaction; eijk is the random residual effect. A detailed comparison of object means was made using Tukey’s test. Differences were considered significant at p < 0.05.

Czyżak-Runowska G., Wójtowski J.A., Gogół D., Wojtczak J., Skrzypczak E, & Stanisławski D. (2020). Properties of Rennet Cheese Made from Whole and Skimmed Summer and Winter Milk on a Traditional Polish Dairy Farm. Animals : an Open Access Journal from MDPI, 10(10), 1794.

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Reliability Assessment of Radiological Measurements

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In this study, reliability was assessed based on the ICCs and a two-way mixed-effects model, assuming a single measurement and absolute agreement.^{17 (link),18 (link)} Using an ICC target value of 0.8, Bonett approximation was employed with 0.2 set as the width of 95% confidence intervals (CIs). The minimum sample size was calculated to be 36.^{19 (link)}Descriptive statistics were used to summarize patient demographics and radiological measurements. The Kolmogorov-Smirnov test was used to verify the normality of the distribution of continuous variables. Descriptive statistics used included the mean, SD, and frequency. A linear mixed model was used to assess the covariate effects and examine the factors that contributed significantly to the pre- and postoperative MHR.
All statistical analyses were performed using the SAS statistical package v. 9.4 (SAS Institute, Cary, North Carolina, USA) and R v. 3.5.1 (R Foundation for Statistical Computing, Vienna, Austria) with the STATS package 2.3. All statistical tests were two-tailed, and CIs were considered to indicate significance when they did not include zero. A p-value < 0.05 was considered to reflect statistical significance.

Min J.J., Kwon S.S., Sung K.H., Lee K.M., Chung C.Y, & Park M.S. (2021). Remodelling of femoral head deformity after hip reconstructive surgery in patients with cerebral palsy. The Bone & Joint Journal, 103-B(1), 198-203.

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Fatty Acid Variation Analysis Protocol

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A total sample size of 120 subjects provided an estimated 80% statistical power to detect as significant (p < 0.05) a between group difference of at least 0.52 standard deviations; for instance, considering the variations reported in Mayneris-Perxachs, 2014 [15 (link)], the minimum detectable difference in palmitic acid variation was estimated as 1.33%.
Continuous variables were presented as mean ± standard deviation and were compared using the t-test for independent samples. Blood FA at T0 and T3 were compared using the paired Student T-Test. Categorical data were compared using the chi-squared test. Associations between variables were determined using Spearman’s rank correlation. A two-sided p-value less than 0.05 was required for statistical significance. All analyses were performed using the SAS statistical package V.9.4 (SAS Institute, Cary, NC, USA).

Giroli M.G., Werba J.P., Risé P., Porro B., Sala A., Amato M., Tremoli E., Bonomi A, & Veglia F. (2021). Effects of Mediterranean Diet or Low-Fat Diet on Blood Fatty Acids in Patients with Coronary Heart Disease. A Randomized Intervention Study. Nutrients, 13(7), 2389.

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Relationship between PCSK9 and Ultrasonographic Markers

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All quantitative variables were reported as mean ± SD. Variables with a skewed distribution were presented as median and interquartile range, and log-transformed before analyses. Categorical variables were reported as frequency and percentage. Trends across PCSK9 quintiles for continuous and categorical variables were assessed by ANCOVA and by Mantel–Haenszel χ²-test, respectively.
Multiple linear regression analysis was used to assess the independent relationship between PCSK9 levels and each ultrasonographic variable. Model 0 was unadjusted; Model 1 was adjusted for age, sex, latitude, and pharmacological treatment (i.e., therapy with statins, or fibrates, or with both statins and fibrates). The repeatability of GSM measurements was assessed by using the Bland–Altman approach [31 (link)]. All the analyses were carried out with SAS statistical package v. 9.4 (SAS Institute Inc., Cary, NC, USA).

Coggi D., Frigerio B., Bonomi A., Ruscica M., Ferri N., Sansaro D., Ravani A., Ferrante P., Damigella M., Veglia F., Capra N., Lupo M.G., Macchi C., Savonen K., Silveira A., Kurl S., Giral P., Pirro M., Strawbridge R.J., Gigante B., Smit A.J., Tremoli E., Amato M, & Baldassarre D. (2021). Relationship between Circulating PCSK9 and Markers of Subclinical Atherosclerosis—The IMPROVE Study. Biomedicines, 9(7), 841.

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Prognostic Factors in dMMR/MSI-H Colon Cancer

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All patients in the analysis had dMMR/MSI-H stage II colon cancer. Univariate and multivariate analyses were conducted to identify the factors associated with patient outcome (OS). The clinical and demographic characteristics of the patients were summarized using descriptive statistics as appropriate for variable type and distribution (chi-square test for categorical variables and ANOVA for numerical variables). Univariate and multivariate Cox-PH models were built to assess the association between patient characteristics and survival. Backward selection with an alpha level of removal of.05 was used in the multivariate analysis. The Kaplan–Meier survival curves were generated with log-rank tests to evaluate the association between adjuvant chemotherapy in high-risk and low-risk cohorts separately. All analyses were performed with a significance level of 0.05 (two-sided) with SAS Statistical Package, v9.4 (SAS institute, Inc., Cary, North Carolina).

Mohamed A., Jiang R., Philip P.A., Diab M., Behera M., Wu C., Alese O., Shaib W.L., Gaines T.M., Balch G.G., El-Rayes B.F, & Akce M. (2021). High-Risk Features Are Prognostic in dMMR/MSI-H Stage II Colon Cancer. Frontiers in Oncology, 11, 755113.

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Metabolic Syndrome and Cancer Risk

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Baseline characteristics of cases and controls were expressed as medians (IQRs) or proportions. Differences between the two groups were determined by the t-test/Wilcoxon tests for continuous variables depending on data distribution and by the Χ² tests for categorical variables.
To evaluate the association of MetSyn and its components in individual and in combination with cancer risk, conditional logistic regression was used to compute ORs and 95% CIs. Both univariate model and multivariate model were used. The multivariate model was first adjusted for education level, smoking status, drinking status and depression scores and then adjusted additionally for marital status. In sensitivity analysis, we used the IPTW (inverse probability of treatment weighting) to check the stability of the results. Restricted cubic splines were used to examine the association of individual MetSyn components with cancer risk assuming linear and non-linear distribution. In order to avoid reverse causality, a sensitivity analysis was conducted, in which cancer cases that occurred in the first 2 years of follow-up (survey at 2013) were excluded.
All analyses were performed using the SAS statistical package V.9.4 (SAS Institute). All p values were based on two-sided tests, with the statistical significance level set to 0.05.

Li L., Meng F., Xu D., Xu L., Qiu J, & Shu X. (2022). Synergism between the metabolic syndrome components and cancer incidence: results from a prospective nested case–control study based on the China Health and Retirement Longitudinal Study (CHARLS). BMJ Open, 12(9), e061362.

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Dampness, Mold, ACOS, and Asthma

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To study potential effects of indoor dampness and mold problems on occurrence of ACOS and asthma-only, we applied multinomial logistic regression by SAS procedure LOGISTIC with glogit link function, using SAS statistical package v.9.4 (SAS Institute Inc., Cary, NC, USA). We adjusted these relations for gender, age, education and smoking as core covariates. When we adjusted the analyses further for parental atopy, keeping pets, and occupational exposures other than mold exposure, the point estimates of ACOS did not change more than 10%. Thus, we decided to present the results adjusted for the core covariates.

Jaakkola M.S., Lajunen T.K, & Jaakkola J.J. (2020). Indoor mold odor in the workplace increases the risk of Asthma-COPD Overlap Syndrome: a population-based incident case–control study. Clinical and Translational Allergy, 10, 3.

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Comparative Analysis of Cardiovascular Biomarkers

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Continuous variables were expressed as mean ± (SD) or median with interquartile range (IQR), if they followed a normal or non-normal distribution, while categorical variables were shown as absolute numbers and percentages. Unpaired t-test or Wilcoxon’s rank-sum test were used to compare continuous variables between healthy subjects and CAD patients while chi-square test or Fisher’s exact test were performed for analyses that involved categorical variables. Comparisons among the groups were performed using ANOVA test for normally distributed variables and Wilcoxon’s rank-sum test for not normally distributed variables; Bonferroni’s correction for multiple comparisons was applied. Correlations between variables were executed using the Pearson’s test or the Spearman’s rank test, as appropriate. Statistical analyses were carried out with the SAS statistical package v.9.4 (SAS Institute Inc., Cary, NC, USA). All tests were two-sided, and p values < 0.05 were considered statistically significant.

Fiorelli S., Cosentino N., Porro B., Fabbiocchi F., Niccoli G., Fracassi F., Capra N., Barbieri S., Crea F., Marenzi G., Cavalca V., Tremoli E, & Eligini S. (2021). Netrin-1 in Atherosclerosis: Relationship between Human Macrophage Intracellular Levels and In Vivo Plaque Morphology. Biomedicines, 9(2), 168.

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Longitudinal Trends in Abuse Prevalence

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SAS statistical package (V.9.4) was used for data analyses (SAS Institute). Missing data were excluded from all analyses. These included: do not know or do not remember, and no responses.
Using the merged database, first, the study years were compared in terms of sociodemographic variables, independent source of income, area deprivation level and family support using χ² tests.
Then, the prevalence rates for each outcome were compared between the study years. For each of the three abuse types, results are presented as percentages (95% CIs). Then, to determine if there had been a change in estimated prevalence over time, OR and 95% CIs for reported experience of each outcome were determined using univariate logistic regression models in the merged database, with the study year as a predictor.
Then, the following steps were taken to address further research questions:
All analyses were conducted with survey procedures to allow for stratification by sample location (three regions), clustering by primary sampling units and weighting of data to account for the number of eligible participants in each household.

Fanslow J., Malihi Z., Hashemi L., Gulliver P, & McIntosh T. (2021). Change in prevalence of psychological and economic abuse, and controlling behaviours against women by an intimate partner in two cross-sectional studies in New Zealand, 2003 and 2019. BMJ Open, 11(3), e044910.

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Tumor Growth Analysis with Mixed-Effects

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The SAS statistical package v9.4 (SAS Institute, Inc., Cary, North Carolina) was used for analyses with a significance level of 0.05. A mixed-effects model was implemented to estimate and compare the growth rate among two experimental groups. The correlation among the repeated measurements in each mouse overtime was accounted for accordingly. The tumor volume was log-transformed to meet the normality and equal variance assumption for the statistical model. The p-value was adjusted for multiple comparisons whenever needed. Kruskal-Wallis p-value was calculated for tumor weight comparison.

Jin R., Liu B., Liu X., Fan Y., Peng W., Huang C., Marcus A.I., Sica G.L., Gilbert-Ross M., Liu Y, & Zhou W. (2020). Leflunomide Suppresses the Growth of LKB1-inactivated Tumors in the Immune-Competent Host and Attenuates Distant Cancer Metastasis. Molecular cancer therapeutics, 20(2), 274-283.

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