Handling missing data in clinical trials: An overview

A major problem in the analysis of clinical trials is missing data caused by patients dropping out of the study before completion. This problem can result in biased treatment comparisons and also impact the overall statistical power of the study This paper discusses some basic issues about missing data as well as potential watch outs. The topic of missing data is often not a major concern until it is time for data collection and data analysis. This paper provides potential design considerations that should be considered in order to mitigate patients from dropping out of a clinical study. In addition, the concept of the missing-data mechanism is discussed. Five general strategies of handling missing data are presented: 1. Complete-case analysis, 2. Weighting methods, 3. Imputation methods, 4. Analyzing data as incomplete, and 5. Other methods. Within each strategy, several methods are presented along with advantages and disadvantages. Also briefly discussed is how, the International Conference on Harmonization (ICN) addresses the issue of missing data. Finally, several of the methods that are illustrated in the paper are compared using a simulated data set.