Itemwise conditionally independent nonresponse modelling for incomplete multivariate data

We introduce a nonresponse mechanism for multivariate missing data in which each study variable and its nonresponse indicator are conditionally independent given the remaining variables and their nonresponse indicators. This is a nonignorable missingness mechanism, in that nonresponse for any item can depend on values of other items that are themselves missing. We show that under this itemwise conditionally independent nonresponse assumption, one can define and identify nonparametric saturated classes of joint multivariate models for the study variables and their missingness indicators.We also showhowto perform sensitivity analysis with respect to violations of the conditional independence assumptions encoded by this missingness mechanism. We illustrate the proposed modelling approach with data analyses.

Duke Scholars

Author Jerome P. Reiter Statistical Science