Request for guidance in analysing this data

Ossy — Thu, 03 Oct 2024 21:04:15 GMT

How can I create new variables, I have attached the dataset.

what is the prevalence of hypertension in the class? (Systolic >120 and Diastolic >90
What is the prevalence of high cholesterol in the class (cholesterol ≥240)?
What is the prevalence of diabetes in the class (glucose ≥126)?
What is the prevalence of obese (BMI) ≥30, based on height and weight in inches
1=Female and 2=Male
Calculate the prevalence of hypertension, diabetes, high cholesterol, and obesity by Sex

Re: Request for guidance in analysing this data

ballardw — Thu, 03 Oct 2024 21:57:17 GMT

Since we do not have your spreadsheet file that code to Import it does not provide a data set.

This sounds like class work. What have you done so far?

A data step is typically used to perform conditional actions like adding a variable to indicate some is "true" or not in a data step. With two variable you use an IF statement that uses both variables and the boundary value to test and set the value of a new variable to indicate if the comparison is of the level of interest or not, typically numeric 1 or zero for true or false. An example:

If var1>somevalue and var2>differentvalue then newvar=1;
else newvar=0;

Comparisons might be =, < , > or GE (greater than or equal) or LE (less than or equal).

Same with single variables but only one comparison is needed.

If othervariable le 16 then anewvariable is 1;
else 0;

Or you can use syntax of Newvariable = (comparison code) as SAS will return 1 for true and 0 for false.

HighChol = (Cholesterol ge 240);

Were you provided a formula to calculate BMI? Calculate BMI and then the same sort of comparison.

After you have the new variables then use Proc freq with a tables that has Sex * condition.

Here is an example with the SASHELP.Class data set:

Proc freq data=sashelp.class;
   tables sex* age;
run;

You will get counts and percentages, overall, row and column of each level of sex compared with each level of age.

When you want to have multiple variables compared use () to group lists:

tables sex * (highchol obese hypertension diabetes);

topic Request for guidance in analysing this data in New SAS User

Request for guidance in analysing this data

Re: Request for guidance in analysing this data