Abstract

Cross-visit checks are a vital part of data cleaning for longitudinal studies. The nature of longitudinal studies encourages repeatedly collecting the same information. Sometimes, these variables are expected to remain static, go away, increase, or decrease over time. This presentation reviews the naïve and the better approaches at handling one-variable and two-variable consistency checks. For a single-variable check, the better approach features the new ALLCOMB function, introduced in SAS® 9.2. For a two-variable check, the better approach uses a BY PROCESSING variable to flag inconsistencies. This paper will provide you the tools to enhance your longitudinal data cleaning process.