![]() Variableĭescription of variables and coding in the input dataset, mydata Instructions for SAS users (Step 3), guidance on renaming and coding variables in your dataset. ![]() If you’re not using SAS or R, you can download CDCref_d.csv and create a program based on cdc-source-code.sas. Note that the z-scores and percentiles calculated for children with obesity will differ from earlier (pre-2022) versions of this SAS program. ![]() The SAS program, cdc-source-code (files are below, in step #1), calculates these z-scores and percentiles for children in your data from the reference data in cdc_ref.sas7bdat for children without obesity and extended BMI percentiles and z-scores for children with obesity. See the section on the extended BMI percentiles and z-scores for more information. These extreme values, however, are not necessarily incorrect and could be reviewed for possible inclusion or exclusion.Īlthough the SAS program calculates z-scores and percentiles for children up to 20 years of age, the World Health Organization (WHO) growth charts are recommended for children = 95 th percentile (1.645 z-score)) changed on Dec 15, 2022, to use extended BMIz. The program also allows for the identification of outliers. In addition, weight-for-height z-scores and percentiles are also calculated. ![]() This SAS program calculates percentiles and z-scores (standard deviations) for a child’s sex and age for BMI, weight, height, and head circumference from the CDC growth charts (1). Note that the calculations for BMI z-scores and percentiles for 2- to 19-year-olds with obesity (BMI ≥ 95 th percentile for a child’s sex and age) have changed on Dec 15, 2022. Extreme values, Implausible Values, and Data Errors. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |