When undertaking confirmatory, possibly model-based analyses of data from clinical trials, statisticians frequently are confronted with having to assess and address potential biases introduced by missing data. New developments for handling missing data have proliferated in the literature. Sensitivity analysis, which allows the assessment of the impact of a wide range of non-ignorable missing patterns on the robustness of the statistical results, provides a unique and powerful alternative for analyzing missing data. An example utilizing sensitivity analysis in a large fracture trial will be described briefly. The practical issues associated with the application of sensitivity analysis will be discussed thereafter.