PURPOSE: This session will highlight the challenges of missing data in observational studies from the design and analysis perspectives.  We will also discuss study design elements and analysis methods to address missing data. DESCRIPTION: Observational studies play a substantial role in both pre-market and post-market safety/effectiveness evaluation of medical products. However, numerous kinds of biases are common in observational studies, which may create difficulties in the interpretation of study results. Capturing data from real world clinical practice potentially increases the amount of missing data. The National Research Council issued a report titled “The Prevention and Treatment of Missing Data in Clinical Trials” in December of 2012, however, the focus of the report was on the assessment of intervention efficacy in confirmatory randomized controlled clinical trials. Understanding the potential sources of missing data from a study whose design imposes structure on data captured from real world clinical practice allows the selection of study design elements that may help reduce the quantity of missing data. Given that missing data exist, analysis methods that support meaningful and valid inferences from observational research are necessary. This workshop starts with an overview of the potential sources of missing data in observational studies, including prospective and retrospective data collection and patient reported outcomes. It then focuses on proactive planning and data collection. Regulatory considerations on how to minimize missing data at study design stage of observational studies will be discussed. The session also aims to discuss the types of missing data in observational studies, statistical methods of testing missing data patterns and handling missing data, when these methods should be applied, and the impact of missing data on the interpretation of study findings. The concepts and methods will be explained with the use of real-world observational study data.