Missing data in primary care research: importance, implications and approaches.

For example, if a patient does not mail in the FOBT kit because they were uncomfortable with specimen collection, we would say that these missing data are MNAR; anytime the missing data are related to what is missing is an indication of MNAR. Inverse probability weighting and multiple imputation approaches are generally recommended as they often assume a MAR missing data mechanism and thus can use supplementary information about the missing data in the final analysis. Performing data checks regularly during the conduct of the study can identify missing data issues and prompt action to address missing data. Dealing with missing data in the conduct phase (during the study) For study designs with prospective data collection, missing data can also occur during the study (prior to analysis). [Extracted from the article]