The reliability and interpretability of results from clinical trials can be significantly diminished by missing data. Frequently used approaches to address these concerns, such as upward adjustments in sample sizes or simplistic imputation methods such as ‘last observation carried forward’, ‘complete case’ or ‘worst case’ analyses, usually are inadequate. Methods for imputation, in addition to being widely understandable, should be based on rational pre-specified approaches that provide a practically achievable reduction in bias. While rational imputation methods are useful, the preferred and often only satisfactory approach to addressing missing data is to prevent it! To meaningfully reduce the level of missing data, it is important to recognize and address many factors that commonly lead to higher levels of missingness. Approaches to reduce missing data will be discussed.