Finally, we compared the predicted mean heart age for participants aged 30 to 74 years with an FHPHD with those without a family history. Based on the Framingham study participants, D'Agostino et al23 presented simple sex‐specific risk functions to evaluate the 10‐year risk of developing overall CVD. The nonlaboratory predictors for the multivariable risk factor algorithm included age, BMI, treated and untreated systolic blood pressure, smoking, and diabetes mellitus. They also introduced heart age, the estimated age of a person's vascular system based on these predictors. The difference between heart age and chronological age provides an effective way to communicate risk for developing CVD.24 We age‐adjusted heart age and excess heart age (defined as the difference between heart age and chronological age) using the age groups 30 to 39, 40 to 49, 50 to 59, and 60 to 74 years and the 2000 US standard population. The survey data were analyzed using SURVEYFREQ and SURVEYLOGISTIC procedures in SAS version 9.3 (SAS Institute, Cary, NC) that takes into account the complex survey design of the NHANES, and the sample weights were adjusted for pooling 4 cycles of NHANES data.
Surveyfreq
Manufactured by SAS Institute
Sourced in United States
SURVEYFREQ is a statistical software module designed for analyzing survey data. It provides functionality for generating frequency tables and conducting statistical tests on categorical variables. The core function of SURVEYFREQ is to assist researchers and analysts in summarizing and interpreting survey data effectively.
Automatically generated - may contain errors
Lab products found in correlation
Sas version 9, by SAS Institute (6 mentions)
Sas 9, by SAS Institute (3 mentions)
Surveylogistic, by SAS Institute (2 mentions)
Version 9, by SAS Institute (1 mentions)
Sas proc surveylogistic, by SAS Institute (1 mentions)
Spss statistics version 28, by IBM (1 mentions)
Proc surveylogistic, by SAS Institute (1 mentions)
10 protocols using surveyfreq
1
Estimating Cardiovascular Disease Risk
2
Impact of SHS on Mental Health
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Since KYRBWS-XIV was a survey based on a complex sampling design using two-stage stratified cluster sampling, the analysis was performed as complex sample estimates using weight (proc surveyfreq, proc surveylogistic, etc. using SAS 9.4(SAS Institute Inc., Cary, NC, USA)). Logistic regression analysis was performed to analyze the association between the level of exposure to SHS and stress, depression, and suicidal ideation using the respondents’ general characteristics of age, gender, education level of parents, school achievement, economic status, inhabitation, and drinking as the control variables; the level of exposure to SHS was used as the independent variable; and stress, depression, and suicidal ideation as the dependent variables.
3
Analyzing Cardiovascular Health Trends
4
Motor Disability and Mental Health Associations
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Standard weighting procedures for constructing sample weights that allowed for complex survey sample designs were used [11 ]. Descriptive statistics were used for population-weighted numbers as well as prevalence of co-morbid mental disability among those with motor disability by different characteristics. The Chi-square test was used to examine differences between the proportion of persons having mental disability among those with and without motor disability. The Taylor series linearization method was used to estimate standard errors of proportions for cross-tabulation tables, allowing for both first-stage cluster and stratum variance, and corresponding 99% confidence interval (CI). Univariate logistic regression and multivariate logistic regression were used to calculate the unadjusted and adjusted odds ratios in the association between related factors and mental disability among individuals with motor disability. SURVEYFREQ, SURVEYLOGISTIC, and version 9.1 SAS packages (SAS Institute, Inc., Cary NC, USA) were used for data analyses. For the large survey size, a two-sided p-value < 0.01 was set as statistical significant.
5
Prevalence of Pain in Korea
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Weightings offered by the KCDC were applied to the data to enhance the representativeness of the sample to the Korean population. The annual frequencies of the pain group and severe pain group were calculated using the survey questionnaire. The aforementioned weightings were applied to each datum to deduct the weighted frequency using SURVEYFREQ (SAS Institute, Cary, NC, USA), and the prevalence of each group was calculated and expressed as a percentile. The analysis was conducted separately by age or sex. Errors related to differences in average age by the year were corrected. Multiple logistic regression analysis was performed to determine possible differences in prevalence by year. Odds ratio was calculated for each model including age and sex. All data were analyzed. Data were interpreted as significant at a P value less than 0.05 using SAS ver. 9.4 (SAS Institute).
6
Cancer Survivors' Health Behaviors by Marital Status
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Using the Rao-Scott design-adjusted chi-square test—a design-adjusted version of the Pearson chi-square test [28 (link)], we calculated weighted percentages (SAS Proc surveyfreq) of survivor health behaviors and compared health behaviors among cancer survivors with different marital status. To examine whether the relationship between health behaviors and marital status differed by cancer type, we conducted weighted multivariable logistic regressions (SAS Proc surveylogistic) and modeled cancer type, marital status, and the cancer type*marital status interaction on health behaviors. We used a weighted regression approach with personal weights to account for MEPS’ design complexity. To examine whether sociodemographic factors influenced the relationship between health behaviors and marital status, we added survivors’ characteristics into the models obtained in the previous steps. We performed stepwise variable selection to obtain parsimonious models.
7
Determinants of Poultry Ownership in Ifanadiana
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A descriptive analysis of household and individual-level characteristics for the population of the Ifanadiana District was performed. Multiple linear regression was used to establish the association between poultry ownership and: 1) the indicator variables of income, physical capital and human capital within district households; and 2) individual-level variables used to create the human capital index, i.e. stunting in children < 5 years, underweight in adults, and the average years of adult education (Appendix A.5). Descriptive statistics and linear regression analyses were performed using the survey procedures (SURVEYMEANS, SURVEYFREQ, SURVEYREG) available in SAS 9.3 (Cary, NC), which account for cluster sample design using Taylor linear approximation for variance estimation. Household weights were applied to provide estimates at the district and commune level.
8
Prevalence Estimation with Complex Sampling
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To generate prevalence estimates, we calculated weighted percentages and 95% CIs using PROC SURVEYFREQ in SAS version 9.4 (SAS Institute) and Complex Samples Frequencies in SPSS statistics version 28 (IBM Corp). To compute adjusted prevalence differences and 95% CIs, we defined outcomes dichotomously and used PROC SURVEYREG, using robust standard errors to correct for design effects and heteroskedasticity in binary outcomes. We considered several models (eMethods in Supplement 1 ), choosing the final model based on concordance with theory, findings from prior research, and fit statistics. P values were corrected for multiple comparisons by controlling the false-discovery rate (FDR) using the Benjamini-Hochberg method.25 (link) The resulting values are known as FDR-adjusted (or FDR-corrected) P values or as q values26 (link); we use the latter term here. These q values represent the probability that the given difference would be a false discovery; they represent the expected proportion of false positives that would be seen among the collection of all differences whose q values were at or below the given q value. A q < .05 indicates statistical significance.
9
Asthma Risk Factors Identification
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Participants were divided in 2 groups based on presence or absence of asthma. Data were analyzed and adjusted for complex sampling design using Statistical Analysis Software SURVEYFREQ procedure (SAS Version 9; SAS Institute, Cary, NC). For categorical variables, we calculated frequencies and percentages, as for continuous variables, we considered means and standard deviations. Asthmatic and non-asthmatic patients’ differences were detected using the chi-square test. Multivariate logistic regression analyses were carried out to identify predictors of asthma. Results were reported as odds ratio (OR), and 95% confidence interval (CI). A p-value < 0.05 was used to indicate statistical significance.
10
Interfacility Transfer Predictors in Clipping or Coiling
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Descriptive statistics summarized cohort subgroups treated with clipping or coiling and bivariate correlations by transfer status. Weights were applied with PROC SUR-VEYFREQ (SAS Institute Inc.). Model variables were chosen with bivariate associations, clinical significance, and correlation using a mix of Pearson, Spearman, Cramer's V, and Kendall's Tau tests, depending on the nature of each variable. Multivariable weighted logistic regression (PROC SURVEYLOGISTIC, SAS Institute Inc.) was applied for a multivariate model of interfacility transfer, using reverse stepwise selection for parsimonious models, stratified by clipping or coiling, and we report odds ratio, confidence intervals, p values, and Wald chi-square statistics to differentiate very low p values.
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