  Sensitivity analysis is popular in dealing with missing data problems
particularly for non-ignorable missingness. It analyses how sensitively the
conclusions may depend on assumptions about missing data e.g. missing data
mechanism (MDM). We called models under certain assumptions sensitivity models.
To make sensitivity analysis useful in practice we need to define some simple
and interpretable statistical quantities to assess the sensitivity models.
However, the assessment is difficult when the missing data mechanism is missing
not at random (MNAR). We propose a novel approach in this paper on attempting
to investigate those assumptions based on the nearest-neighbour (KNN) distances
of simulated datasets from various MNAR models. The method is generic and it
has been applied successfully to several specific models in this paper
including meta-analysis model with publication bias, analysis of incomplete
longitudinal data and regression analysis with non-ignorable missing
covariates.
