TY  - JOUR
T1  - Itemwise conditionally independent nonresponse modeling for incomplete multivariate data
JF  - Biometrika
Y1  - 2017
A1  - M. Sadinle
A1  - J.P. Reiter
KW  - Loglinear model
KW  - Missing not at random
KW  - Missingness mechanism
KW  - Nonignorable
KW  - Nonparametric saturated
KW  - Sensitivity analysis
AB  - 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 show how to perform sensitivity analysis to violations of the conditional independence assumptions encoded by this missingness mechanism. Throughout, we illustrate the use of this modeling approach with data analyses.
VL  - 104
UR  - https://doi.org/10.1093/biomet/asw063
IS  - 1
ER  -