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Data Management : How Raw Variables with Complex Skip Patterns and Missing Values are Interpreted with Derived Variables

Shelter, Health, and Drug Outcome in Women (SHADOW) is a research study with the Department of Medicine at the University of California, San Francisco that has been gathering quantitative information through biannual interviews with impoverished women in San Francisco. The project seeks to understand how competing needs influence the health and HIV risk behaviors of homeless women in order to inform healthcare delivery and social services. The focus of this paper is to employ data management techniques in SAS to create common variables of interest that may be requested by study investigators. A question of particular importance is whether a participant partakes in serodiscordant sex (i.e. sex with a man of the opposite HIV status). Roughly half of the 301 women in the study are HIV positive, and multiple conditions must be met for a person of either serostatus to be eligible for the outcome. Ensuring the raw data are transformed into accurately created derived variables and easily understood reports is a necessity of which the use of SAS has many advantages. WARNING: This presentation is based on data from a research study regarding HIV risk. It contains language regarding sex that some individuals may find offensive. For this reason words in brackets denote substitutions for the original names.