Handling missing data is an important consideration in the analysis of data from all kinds of medical device studies. Missing data in medical device studies can arise for all the reasons one might expect in pharmaceutical clinical trials. In addition, they occur by design, in nonrandomized device studies, and in evaluations of diagnostic tests. For dichotomous endpoints, a tipping point analysis can be used to examine nonparametrically the sensitivity of conclusions to missing data. In general, sensitivity analysis is an important tool to study deviations from simple assumptions about missing data, such as the data being missing at random. Approaches to missing data in Bayesian trials are discussed, including sensitivity analysis. Many types of missing data that can occur with diagnostic test evaluations are surveyed. Careful planning and conduct are recommended to minimize missing data. Although difficult, the prespecification of all missing data analysis strategies is encouraged before any data are collected.