Missing Data and Imputation

When you conceive, design, and implement an orthopedic surgical trial, there are many steps to follow to reduce the risk of bias that may occur during the process. One of the biggest enemies of the validity of any clinical research from a simple case series to the most sophisticated meta-analysis is missing data. Statistical power and the validity of a study are based on having complete information for all study subjects at all study time points. If data is not captured completely or patients miss follow-up visits, the integrity of the study begins to be compromised. Missing data, or missing values, occur when no data value is stored for the variable in an observation. Missing data is a common occurrence and can have a significant effect on the conclusions that can be drawn from a study. Often, especially in orthopedic research, a lot of time and resources are needed to accomplish a complete and well-designed study, but missing data and/or patients lost to follow-up for studies with long follow-up in particular, may corrode the validity of the study findings to a point that the results cannot be trusted.