Missing data are common in clinical research, particularly for variables requiring complex, time-sensitive, resource-intensive, or longitudinal data collection methods. However, even seemingly readily available information can be missing. There are many reasons for “missingness,” including missed study visits, patients lost to follow-up, missing information in source documents, lack of availability (eg, laboratory tests that were not performed), and clinical scenarios preventing collection of certain variables (eg, missing coma scale data in sedated patients). It is particularly challenging to interpret studies when primary outcome data are missing. However, many methods commonly used for handling missing values during data analysis can yield biased results, decrease study power, or lead to underestimates of uncertainty, all reducing the chance of drawing valid conclusions.
Newgard CD, Lewis RJ. Missing Data: How to Best Account for What Is Not Known. JAMA. 2015;314(9):940–941. doi:10.1001/jama.2015.10516
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