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 |