Sas software 9.4 version

Manufactured by SAS Institute

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SAS software 9.4 version is a comprehensive statistical analysis and data management software package. It provides tools for data manipulation, statistical modeling, reporting, and analytics. The software is designed to handle large and complex data sets from various sources.

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15 protocols using sas software 9.4 version

Genetic Factors in Neuroblastoma Risk

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The deviation of the SNP genotypes from the Hardy-Weinberg equilibrium was assessed using the goodness-of-fit χ² test in controls. Two-sided chi-square test was conducted to compare the distribution of demographic variables and allele frequencies between the two groups. Odds ratios (ORs), 95% confidence intervals (CIs) and P values were calculated. Testing for association between SNPs and neuroblastoma risk was performed using unconditional logistic regression analysis with adjustment for age and gender. Stratified analyses were also conducted by age, gender, tumor sites, and clinical stages. Analyses were carried out using the version 9.4 SAS software (SAS Institute, Cary, NC). The significant threshold was P< 0.05 (two-sided).

Cheng J., Zhuo Z., Zhao P., Zhu J., Xin Y., Zhang J., Li P., Gao Y., He J, & Zheng B. (2019). PARP1 gene polymorphisms and neuroblastoma susceptibility in Chinese children. Journal of Cancer, 10(18), 4159-4164.

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Allergy Risk Association with SNPs

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The goodness-of-fit χ² test was used to verify whether the included SNPs were in Hardy–Weinberg equilibrium (HWE) among the control subjects. The distributional differences of demographic characteristics and allele frequencies between AR cases and controls were evaluated by the two-sided chi-square test. The associations between selected SNPs and AR risk were assessed by calculating the odds ratios (ORs) and 95% confidence intervals (CIs) in an unconditional logistic regression model. And the adjusted ORs and corresponding 95% CIs that adjusted for age and sex were calculated through unconditional multivariate logistic regression analysis. Furthermore, stratification analysis was conducted according to age, gender, and clinical grading.
The version 9.4 SAS software (SAS Institute, NC, USA) was used for conducting all statistical analyses in this study. It would be considered a statistically significant result when P-value < 0.05.

Liu W., Zeng Q., Zeng Y., Tang Y, & Luo R. (2022). Association between the genetic variants of base excision repair pathway genes and allergic rhinitis susceptibility in Chinese children. The World Allergy Organization Journal, 15(5), 100650.

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Genetic Variants and Ovarian Cancer

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Departures from Hardy-Weinberg equilibrium (HWE) were evaluated for each SNP in controls by goodness-of-fit χ² test. Two-sidedχ² test and t test were performed, as appropriate to compare the demographic variables and allele frequencies between the cases and the control group. The odds ratio (OR), and the corresponding 95% confidence interval (CI) for each SNP were analyzed. Logistic regression analysis was used to assess the correlation between SNPs and epithelial ovarian cancer susceptibility. Statistical adjustment for age was performed. The version 9.4 SAS software (SAS Institute, Cary, NC) was used to perform analysis. The significant threshold was P < 0.05.

Zhou Z., Ou X., Zou Q., Chu L., Quan X., Chen Y, & Liu Y. (2020). Functional polymorphisms in FOXC2 gene and Epithelial ovarian Cancer susceptibility in Chinese population. Journal of Ovarian Research, 13, 34.

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Erythropoietin and Age-Related Macular Degeneration

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All variables were presented with numbers and percentages, compared between EPO user and non-EPO user cohorts. The standardized difference was estimated to show the balance of variables between two cohorts, the standardized difference over 0.1 was considered a significant difference. We calculated the incidence of AMD using the number of individuals diagnosed with AMD divided by the follow-up duration (person-year) for both cohorts. Univariate and multivariate Cox proportional hazards regression models with time-dependent covariate were used to calculate the EPO cohort to the comparison cohort crude hazard ratios (cHRs) and adjusted hazard ratios (aHRs) of AMD, respectively. The aHRs were calculated after controlling for sex, age, and all comorbidities in the multivariate Cox models. The association between EPO treatment and AMD risk was assessed not only by the EPO dosage, but also by the AMD subtype. To observe the dose–response relationship between EPO dosage and risk of AMD, the linear trend test was used to estimate the potential trends. We used the Version 9.4 SAS software (SAS Institute Inc., Cary, NC, USA) for data analyses. Unless otherwise specified, a p value of <0.05 was considered statistically significant.

Huang E.J., Sung F.C., Hung P.H., Muo C.H., Wu M.M, & Yeh C.C. (2022). The Association of Erythropoietin and Age-Related Macular Degeneration in Hemodialysis Patients: A Nationwide Population-Based Cohort Study. International Journal of Molecular Sciences, 23(17), 9634.

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Longitudinal Analysis of Factors Affecting Medical Outcomes

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A generalized estimating equation (GEE) Poisson regression was applied for the analysis. Poisson regression with a log link function and an unstructured (UN) working correlation matrix, which had the lowest Quasi-likelihood under Independence Model Criterion (QIC) statistics, was used for the longitudinal data (from 2018 to 2021). The results are presented as adjusted relative risk (aRR) with 95% confidence intervals (CI). Subgroup analyses were conducted to determine the detailed effects based on transfer time to the medical institutions and the covariates. As the variance inflation factors (VIF) for all variables were less than 1.6, there was no evidence of multicollinearity. Version 9.4 SAS software (SAS Institute, Cary, North Carolina, USA) was used. Statistical significance was set at P ≤ 0.05.

Kim J., Kim H., Park E.C, & Jang S.I. (2023). Effect of on-site first aid for industrial injuries on healthcare utilization after medical treatment: a 4-year retrospective longitudinal study. Journal of Occupational Medicine and Toxicology (London, England), 18, 12.

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Randomized Design and ANOVA Analysis

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The data were subjected to a completely randomized design and the data obtained were analyzed using one-way analysis of variance (ANOVA) by a general linear model (GLM) procedure in SAS software 9.4 Version (SAS Institute Inc., Cary, NC, USA). The statistical model used was: Y_ij = µ + T_i + e_ij. Where Y_ij is the mean of the j-th observation of the i-th treatment; µ is the sample mean; T_i is the effect of the i-th treatment; and e_ij is the effect of the error. Histogram distribution and quantile-quantile (Q-Q) plots of the model were used for the assumption of normality. Duncan multiple range test was used to separate means at p < 0.05 significance level. The results are presented as mean ± SEM in all tables.

Muhammad A.I., Mohamed D.A., Chwen L.T., Akit H, & Samsudin A.A. (2021). Effect of Selenium Sources on Laying Performance, Egg Quality Characteristics, Intestinal Morphology, Microbial Population and Digesta Volatile Fatty Acids in Laying Hens. Animals : an Open Access Journal from MDPI, 11(6), 1681.

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Oral Cancer Mortality Trend Analysis

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The ITS model was constructed using the log-linear regression model with a simple linear spline function:

\log (r_{i j}) = μ + α_{i} + β_{1} T_{j} + β_{2} {(T_{j} - t_{1})}_{+} + β_{3} {(T_{j} - t_{2})}_{+} + ε_{i j},

where

r_{i j}

denotes the oral cancer mortality rate at the ith age group (i = 1, 2, …, I) and jth period group (j = 1, 2, …, J),

μ

is the intercept,

α_{i}

is the age effect at the ith age group,

T_{j} = j

{(T_{j} - t_{1})}_{+} = \max {0, (T_{j} - t_{1})}

and

{(T_{j} - t_{2})}_{+} = \max {0, (T_{j} - t_{2})}

are the spline functions which use two knots of

t_{1}

and

t_{2}

as the time points of 1999 and 2007, respectively. Here, 1999 was the launch year of the screening program, and 2007 was the year the screening coverage rates began to rise (see Figure S3 in the Supplementary Material). The parameter

β_{1}

refers to the linear slope of oral cancer mortality from 1991–1999,

β_{2}

refers to the slope changes of oral cancer mortality from 1999 to 2007, and

β_{3}

refers to the slope changes of oral cancer mortality after 2007. All data organization and statistical analysis were performed by using SAS software 9.4 version (SAS Institute Inc, Cary, NC, USA).

, & Su S.Y. (2022). Evaluation of Nationwide Oral Mucosal Screening Program for Oral Cancer Mortality among Men in Taiwan. International Journal of Environmental Research and Public Health, 19(21), 14329.

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Factors Associated with Good Outcome

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Statistics were performed using SAS software 9.4 version (SAS Institute Inc., Cary, NC, USA). Categorical variables are displayed as the number and percentage of patients (n, %), and quantitative variables are presented as the median and interquartile range.
We explored factors associated with good outcome using univariate and multivariate stepwise logistic regression. Crude and adjusted odds ratios (aOR) are given with their 95% confidence interval (95% CI). We used a structured approach to select variables to be included in the multivariate model, associating literature search and exchange between experts in order to identify the relationships between the variables and include relevant factors in the model (patient and stroke characteristics). We entered in the multivariate model baseline demographic, clinical, and radiological characteristics (age, sex, center, pre-stroke mRS, NIHSS and ASPECT scores, and occlusion site), stroke risk factors, procedural variables (intravenous thrombolysis, successful recanalization, and time from admission to groin puncture), and ICH as explanatory variables. Interactions were tested and multicollinearity was screened using the COLLIN option on SAS. Significance level was set at α = 0.05.

Derex L., Haesebaert J., Odier C., Alesefir W., Berthezène Y., Buisson M., Daneault N., Deschaintre Y., Eker O.F., Gioia L., Iancu D., Jacquin G., Karkri F., Lapierre M., Nighoghossian N., Raymond J., Roy D., Stapf C., Weill A, & Poppe A.Y. (2022). Predictors of Outcome After Mechanical Thrombectomy in Stroke Patients Aged ≥85 Years. The Canadian journal of neurological sciences. Le journal canadien des sciences neurologiques, 49(1).

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Analyzing Immune Markers in SLE Patients

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Data are expressed as the mean ± standard deviation (SD). Numerical data between patients and HC or between different patient groups were analyzed by Mann–Whitney U test. Correlation analysis was performed by Spearman correlation coefficient test. For comparing SNHG16, TLR4 and TRAF6 levels in PBMC or PBN from SLE and HC, and SNHG16, TLR4 and TRAF6 levels in PBMC or PBN from SLE and SLEDAI-2K, multivariable analysis adjusted for age/sex or plus medications were performed by SAS software 9.4 version (SAS Institute Inc, Cary, NC, USA). Hemorrhage frequencies in the lungs between different mouse groups were compared by Fisher’s exact test. Differences in other analyses were determined by Student’s t test. P values less than 0.05 were considered significant in this study with symbols as * for p < 0.05, **p < 0.01 and *** for p < 0.001.

Hsieh Y.T., Chen Y.C., Chou Y.C., Kuo P., Yen Y.T., Tsai H.W, & Wang C.R. (2023). Long noncoding RNA SNHG16 regulates TLR4-mediated autophagy and NETosis formation in alveolar hemorrhage associated with systemic lupus erythematosus. Journal of Biomedical Science, 30, 78.

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Association between Health Screening and Outcomes

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All statistical analyses were performed using SAS software 9.4 version (SAS Institute Inc., Cary, NC, USA). After the distribution of variables was standardized, continuous variables were subjected to t-tests and categorical variables were subjected to chi-square tests. Multiple logistic regression analyses were performed after adjusting for age, duration of stay, nationality, residence, economic status, eligibility for general health screening, occupation, and comorbidity. To account for the effect of general health screening, participation in the general health screening was stratified, and multiple logistic regression was performed.

Chang H.K., Seo S.S., Myong J.P., Koo J.W, & Jeong J. (2018). Factors Associated with Cervical Cancer Screening among Married Female Immigrants with Korean Husbands in South Korea. International Journal of Environmental Research and Public Health, 15(11), 2528.

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