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 -