Sensitivity analysis of nonignorable coarsening

Missing and censored data are common types of coarse data. An important consequence of the stochastic nature of the coarsening process is nonignorability. Failure to properly account for a nonignorable coarsening mechanism could vitiate inferences. One approach to this problem is to perform a sensitivity analysis to see how inferences change when the coarsening mechanism departs from ignorability. In this dissertation, I apply a simple sensitivity analysis tool, the index of sensitivity to nonignorability (ISNI, Troxel et al. 2004), to the evaluation of nonignorability of the coarsening process based on the general coarse data model (Heitjan and Rubin, 1991). Moreover, I extend ISNI for MLE to ISNI for Bayesian inference. I also propose a graphical method to check sensitivity, which can be used as a first step in judging the robustness of key inferences to nonignorable coarsening. Simulation studies show that this sensitivity analysis procedure is valid for practical use. I illustrate the procedure through application to two real data sets, one involving censoring by end of study in an randomize clinical trial and the other involving competing risks in an observational study.