Missing data can jeopardize the interpretation of results from a study and can ultimately be detrimental to the overall success of a study. Missing data can come in different forms, such as informative missing (eg, due to toxicity) or operational missing (eg, lax monitoring). As no statistical analysis can fully account for the effect of missing data, the best solution is prevention. During this session, we will discuss the following: 1. Examples of studies where missing data have prevented interpretation of results and ideas on how this may have been avoided. 2. Protocol and Informed Consent Process-What additional information can be provided to the investigators and patients to limit missing data? 3. CRF-What additional information can be collected in the CRF to inform reasons for missing data? 4. Follow-up-What additional data should be collected on subjects who discontinue?