Surveylogistic

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SURVEYLOGISTIC is a procedure in the SAS/STAT software that provides regression modeling for survey data. It allows users to fit logistic regression models for sample survey data, taking into account the sampling design.

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Lab products found in correlation

Surveyfreq, by SAS Institute (2 mentions) Sas version 9, by SAS Institute (2 mentions) Sas 9, by SAS Institute (2 mentions) Version 9, by SAS Institute (1 mentions)

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SAS PROC SURVEYLOGISTIC PROC SURVEYLOGISTIC

3 protocols using surveylogistic

Analyzing Cardiovascular Health Trends

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SAS version 9.3 (SAS Institute, Cary, NC) was used to perform the statistical analysis. Estimates for NHANES 1999–2002 were weighted using 4-year weights provided by the National Center for Health Statistics based on where the information was obtained. Estimates for NHANES 2009–2012 were weighted using 4-year weights calculated according to the method provided by the National Center for Health Statistics. Those weights were incorporated to account for the complex survey design (including oversampling), survey nonresponse, and poststratification. Presence of difference between the two NHANES periods was determined using a χ² analysis with SURVEYFREQ (SAS Institute, Cary, NC).
To further analyze the data, we constructed a new dichotomous variable for each of seven components of the metrics based on our modified cardiovascular health metrics. Three health factors and healthy weight were recoded using “1” for ideal category and “0” for the other categories. As for the remaining three health behavior components, we combined categories of ideal and intermediate as “1” and used “0” for the category of poor. A logistic regression analysis using SURVEYLOGISTIC (SAS Institute) was then conducted to determine whether the results were consistent with the χ² analysis when adjusted for age, sex, race, education and poverty level. P < 0.05 was used to count for statistical significance.

King D.E., Xiang J, & Kulshreshtha A. (2016). Cardiovascular Health Status in Baby Boomers with Diabetes Mellitus. Southern medical journal, 109(6), 346-350.

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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.

Guo C., He P., Song X., Chen G, & Zheng X. (2018). Co-morbid mental disability among Chinese elderly with motor disability: Based on a nationwide survey. PLoS ONE, 13(4), e0195555.

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Respiratory Symptoms and Smoking Status

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Respondents were excluded from analysis if data were missing for smoking status, smoking duration, or one or more respiratory symptoms. All analyses was conducted using SAS 9.4 to account for the complex BRFSS sampling. BRFSS method applies an iterative proportional fitting, or raking to address the nonresponsive bias. This method allows for education level, marital status, and home ownership in addition to other traditional demographic characteristics used in post-stratification to increase the representativeness of estimates. Analysis was done collectively for all states and for selected measures by individual state. First, we calculated the percentage and 95% confidence intervals (CI) of sociodemographics, health behaviors, health impairment, and chronic diseases. For comparisons of prevalence between subgroups, statistical significance was determined by t-tests. Prevalence was compared by risk status using pairwise t-tests. A P-value < 0.05 was considered statistically significant for all tests. Other than an age-specific category, all prevalence estimates were age-adjusted to the 2000 US standard population. A trend analysis of each symptom with age and tobacco duration was performed using models regressing moderate or severe SOB, cough, and DOE with each measure using the Wald test of linearity and odds ratio for age increment (SAS Institute, SURVEYLOGISTIC).

Pleasants R.A., Heidari K., Ohar J., Donohue J.F., Lugogo N.L., Kanotra S.M., Kraft M., Mannino D.M, & Strange C.B. (2022). Respiratory Symptoms among US Adults: a Cross-Sectional Health Survey Study. Pulmonary Therapy, 8(3), 255-268.

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