The phenomenon of missing data is ubiquitous in clinical studies. Both the extent of missing data and the structure of missing data can introduce bias into study results and lead to wrong conclusions. Medical writers should be aware of the extent of missing data and should describe the methods used to deal with the issue. This article outlines some of the most commonly used statistical methods for handling missing data. The traditionally used last-observation-carried-forward (LOCF) method to fill data gaps is problematic in many ways. It is better to employ a method that reduces bias, such as multiple imputation (MI) or mixed-effects models for repeated measures (MMRM). Clinical study design can also help minimise the quantity of missing data.