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Proc surveyfreq

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PROC SURVEYFREQ is a statistical procedure in the SAS software suite that analyzes frequency data from complex survey designs. It provides estimates of population totals, means, and proportions, along with their standard errors. PROC SURVEYFREQ generates tables of frequency counts and associated statistics, such as chi-square tests, for categorical variables.

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Sas version 9, by SAS Institute (6 mentions) Sas 9, by SAS Institute (3 mentions) Proc surveyreg, by SAS Institute (2 mentions) Sas system for windows, by SAS Institute (2 mentions) Proc surveymeans, by SAS Institute (1 mentions)

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SAS PROC SURVEYFREQ SURVEYFREQ

7 protocols using proc surveyfreq

1

Demographic Differences in Sexual Risk Behaviors

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Adjusted and weighted prevalence rates were conducted using a weighting factor in YRBSS to provide nationally representative estimates and using PROC SURVEYFREQ in SAS version 9.4 (SAS Institute, Cary, NC) to account for complex sample design, 3-stage cluster sampling design. A weighting factor in YRBSS data was made to adjust for school and student nonresponse, sex, grade, and race/ethnicity. Rao-Scott chi-square tests, which are adjusted for the complex sample design, using PROC SURVERYFREQ and multiple logistic regression analyses using PROC SURVEYLOGISTIC were conducted to access if there were any demographic differences in sexual risk behaviors and in non-sexual risk behaviors; and if obesity was associated with sexual risk behaviors.
Lott L., Sung J.H, & Lee J.E. (2018). Association of adolescent obesity with risky sexual behaviors: Results from Mississippi YRBS, 2013-2015. International journal of advanced research in science, engineering and technology, 5(9), 6743-6747.
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2

Prevalence and Factors of Dizziness

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We calculated the prevalence and 95% confidence intervals (CIs) for dizziness, and potentially associated factors were evaluated by univariable analysis. Only variables with P-value ≤0.05 were selected for multivariable analysis in the logistic regression model. In univariable analysis, the Rao-Scott chi-square test using PROC SURVEYFREQ in SAS version 9.3 (SAS Institute Inc., Cary, NC, USA), and logistic regression analysis using PROC SURVEYLOGISTIC in SAS, were used to test the association between dizziness and risk factors in a complex sampling design. In multivariable analysis, adjusted odds ratios (ORs) with 95% CIs were calculated using logistic regression analysis (PROC SURVEYLOGISTIC in SAS). To reflect national population estimates, sample weights were applied in all analyses. All P-values were two-sided, and P < 0.05 was considered to be statistically significant.
Chang J., Hwang S.Y., Park S.K., Kim J.H., Kim H.J., Chae S.W, & Song J.J. (2018). Prevalence of Dizziness and Associated Factors in South Korea: A Cross-Sectional Survey From 2010 to 2012. Journal of Epidemiology, 28(4), 176-184.
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3

Household Finances and Health Utility

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We conducted multivariate weighted least squares (WLS) regressions to examine the associations between health utility and household finances while adjusted for age, sex, race, and education. Household finances were assessed as household assets and income (Model 1), DPB (Model 2), or household assets and income and DPB (Model 3). Analyses were performed using SAS System for Windows (Version 9.3) procedures that incorporate survey weights, PROC SURVEYFREQ, and PROC SURVEYREG (SAS Institute Inc.).
Sensitivity analyses also included eleven separate chronic health conditions in models 1, 2, and 3.
Hanmer J, & Cherepanov D. (2016). A single question about a respondent’s perceived financial ability to pay monthly bills explains more variance in health utility scores than absolute income and assets questions. Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation, 25(9), 2233-2237.
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4

National Estimates of Continuous and Categorical Variables

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Continuous variables are presented as mean±SD or median with interquartile range (IQR) as appropriate, and compared using Student t tests or Mann-Whitney U tests in unadjusted analyses, respectively. Categorical variables are presented as frequencies and percentages and compared using χ 2 or Fisher exact tests as appropriate in unadjusted analyses. National estimates were obtained using discharge weights provided by Healthcare Cost and Utilization's Project. We used Proc Surveyfreq (SAS Institute Inc, Cary, NC) to account for clustering and the stratified design of the NRD.
Lima F.V., Kolte D., Kennedy K.F., Wang L.J., Abbott J.D., Soukas P.A, & Aronow H.D. (2020). Thirty-Day Readmissions After Carotid Artery Stenting Versus Endarterectomy: Analysis of the 2013-2014 Nationwide Readmissions Database. Circulation. Cardiovascular interventions, 13(4).
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5

Disability Risk Factors by Sex and Nativity

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Chi-square, analysis of variance (ANOVA), and t test tests were used to examine the distribution of covariates for subjects by sex and nativity. Weighted logistic regression analysis was used to assess the independent effect of sex and nativity on ADL disability and mobility disability, controlling for age, education, marital status, language of interview, household income, living arrangement, and medical conditions (arthritis, diabetes, heart attack, stroke, cancer, and hip fracture). Three models were performed. Model 1 included age, sex, nativity, education, marital status, language of interview, household income, and living arrangement. In Model 2, medical conditions were added to Model 1. In Model 3, an interaction term between nativity and sex was added to the variables in Model 2. All analyses were performed using the SAS 9.2 survey procedures (PROC SURVEYFREQ, PROC SURVEYLOGIST, SURVEYREG) (SAS Institute, Cary, NC) to account for design effects and sampling weight. The selected alpha level for statistical significance was 0.05.
Nam S., Snih S.A, & Markides K.S. (2015). Sex, Nativity, and Disability in Older Mexican Americans. Journal of the American Geriatrics Society, 63(12), 2596-2600.
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6

COVID-19 Pandemic's Impact on Metabolic Syndrome

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All statistical analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). Changes in MetS prevalence were stratified by sex and age because the effects of the COVID-19 pandemic have been reported to vary according to sex and age. We also performed pre-specified subgroup analyses based on marital status, education level, household income, and occupation. The values for categorical variables were calculated using SAS (PROC SURVEYFREQ; SAS Institute Inc.), and the values for continuous variables were similarly calculated using SAS (PROC SURVEYMEANS; SAS Institute Inc.). If p-value <0.05, the difference was considered to be statistically significant.
Kwon J.Y, & Song S.W. (2022). Changes in the prevalence of metabolic syndrome in Korean adults after the COVID-19 outbreak. Epidemiology and Health, 44, e2022101.
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7

Epidemiology of Chronic Lymphocytic Thyroiditis

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The baseline study characteristics are presented as mean (SE) for continuous variables and as the percentage (SE) for categorical variables. A linear regression model using “PROC SURVEYREG” in SAS 9.2 (SAS Institute Inc., Cary, NC) was used to test for linear trends over time and the Rao-Scott chi-square test using “PROC SURVEYFREQ” in SAS 9.2 (SAS Institute Inc., Cary, NC) were used. We calculated the age-standardized prevalence and the 95% confidence intervals (CIs) for chronic lymphocytic thyroiditis by gender and year using direct standardization with the world standard population (using “PROC SURVEYREG” in SAS 9.2). We also used binomial regression with a log link to calculate the age-adjusted prevalence ratios and 95% CIs for chronic lymphocytic thyroiditis by gender [24] (link). Additionally, we considered years as a continuous variable when testing for linear trends in the age-adjusted prevalence ratio of chronic lymphocytic thyroiditis by sex. A p<0.05 was considered statistically significant.
Oh C.M., Park S., Lee J.Y., Won Y.J., Shin A., Kong H.J., Choi K.S., Lee Y.J., Chung K.W, & Jung K.W. (2014). Increased Prevalence of Chronic Lymphocytic Thyroiditis in Korean Patients with Papillary Thyroid Cancer. PLoS ONE, 9(6), e99054.
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