Handling Missing Data in Cleaning Workflows

Missing data are not all the same. A value may be missing because it was not collected, because it was not applicable, because the participant refused to answer, because the result is pending, because a visit was missed, because a specimen was lost, because the field was accidentally skipped, or because the export excluded the field. Treating all missing values as identical can lead to poor decisions. Clinical data management requires careful classification of missingness before deciding whether to query, derive, ignore, code, or escalate [@little2019missing; @vanbuuren2018missing].