Exploring sensitivity to unverifiable missing data assumptions, characterizing the uncertainty about these assumptions, and incorporating subjective beliefs about the distribution of missing responses, are key elements in any analysis of incomplete data. The Bayesian paradigm provides a natural venue for accomplishing these aims through the construction and integration of prior information and beliefs into the analysis.