A scalable approach to measuring the impact of nonignorable nonresponse with an EMA application.

ABSTRACT: There is often a need to assess the dependence of standard analyses on the strong untestable assumption of ignorable missingness. To tackle this problem, past research developed simple sensitivity index measures assuming a linear impact of nonignorability and missingness in outcomes only. These restrictions limit their applicability for studies with missingness in both outcome and covariates. Nonignorable missingness in this setting poses significant new analytic challenges and calls for more general and flexible methods that are also computationally tractable even for large datasets. In this paper, we relax the restrictions of extant linear sensitivity index methods and develop nonlinear sensitivity indices that maintain computational simplicity and perform equally well when the impact of

SUBMITTER: Gao W 

PROVIDER: S-EPMC5135668 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature