Estimating Prevalence and Examining Relationships Using Supplement Data

Purpose

National estimates of supplement use may be calculated using NHANES data. This module will introduce how to obtain estimates of the prevalence of supplement use, and how to examine the relationship between supplement use and a categorical or continuous outcome.

 

Task 1: Estimating Prevalence of Supplement Use

Proportions of participants who report supplement use on the NHANES survey may be used as an estimate of the prevalence of supplement use in the US.

• Key Concepts about Estimating Prevalence of Supplement Use Using Proportions
• How to Estimate Prevalence of Supplement Use Using Proportions Using SUDAAN
• How to Estimate Prevalence of Supplement Use Using Proportions Using SAS Survey Procedures
• Download Sample Code and Datasets

 

Task 2: Examining the Relationship Between Supplement Use and a Categorical Outcome using a Chi-Square Test

When interest is in examining the relationship between supplement use and a categorical outcome, a chi-square test may be used to test the association between supplement use and another categorical variable in a two-way table.

• Key Concepts about Examining the Relationship Between Supplement Use and a Categorical Outcome Using a Chi-Square Test
• How to Calculate a Chi-Square Test Using SUDAAN
• How to Calculate a Chi-Square Test Using SAS
• Download Sample Code and Datasets

 

Task 3: Examining the Relationship Between Supplement Use and a Dichotomous Outcome using Logistic Regression

Logistic regression may be used when there is interest in adjusting the relationship between supplement use and a categorical outcome for the effect of other covariates. Logistic regression is a statistical method used to assess the likelihood of a disease or health condition as a function of a risk factor (and covariates). There are two kinds of logistic regression, simple and multiple. Both simple and multiple logistic regression, assess the association between independent variable(s) (X_j) — sometimes called exposure or predictor variables — and a dichotomous dependent variable (Y)  — sometimes called the outcome or response variable.

• Key Concepts about Using Logistic Regression In NHANES
• How to Perform Logistic Regression Using SUDAAN
• How to Perform Logistic Regression using SAS Survey Procedures
• Download Sample Code and Datasets

 

Task 4: Examining the Relationship between Supplement Use and a Continuous Outcome Using a T-test

The t-test is used to test the null hypothesis that the means or proportions of two population subgroups are equal or, equivalently, that the difference between two means or proportions equals zero. It is appropriate in cases where a small number (<30) of degrees of freedom are available, which is the case for the NHANES sample. This tutorial shows how to calculate a t-statistic for a sub-population using SUDAAN and SAS version 9.2.

• Key Concepts about Using the T-Test Statistic
• How to Set Up a T-Test in NHANES Using SUDAAN
• How to Set Up a T-Test in NHANES Using SAS Survey Procedures
• Download Sample Code and Datasets

 

Task 5: Examining the Relationship between Supplement Use and a Continuous Outcome Using Multiple Regression

Linear Regression models, both simple and multiple, assess the association between independent variable(s) (X_i) — sometimes called exposure or predictor variables — and a continuous dependent variable (Y) — sometimes called the outcome or response variable. In cross-sectional surveys such as NHANES, linear regression analyses can be used to examine associations between covariates and health outcomes.  In this Module, supplement use is the primary independent variable of interest. This Task will give examples using SUDAAN and SAS version 9.2.

• Key Concepts about Linear Regression
• How to Perform Linear Regression Using SUDAAN Code
• How to Perform Linear Regression Using SAS
• Download Sample Code and Datasets