In nonexperimental research, missing data pose an increased threat to validity compared with well-conducted randomized clinical trials. However, nonexperimental real-world studies can compensate for these limitations by providing a more accurate picture of how well medical interventions work in real-life settings and for diverse patient populations. Recognizing that some amount of missing data is inevitable, whether for the exposure, outcomes or confounding factors, it is best to plan for missing data at the start of a study by preventing missing data where possible and planning for handling missing data for important variables. In situations where all data elements are not available for all patients, careful examination and handling of missing data is required. Statistical techniques such as multiple imputation may be used to fill in gaps. These methods require understanding, to the extent possible, of assumptions concerning the factors leading to the missing values and how they relate to the study outcomes. Here we describe strategies to prevent missing data and analytic methods to handle missing data in nonexperimental real-world studies, and include an illustration.


Authors: Christina Mack, Zhaohui Su, Aaron Mendelsohn, and Nancy Dreyer