Online Program Home
My Program

Abstract Details

Activity Number: 478 - Missing Data
Type: Contributed
Date/Time: Wednesday, August 1, 2018 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract #329961
Title: A Comparison of Multiple Imputation by Fully Conditional Specification and Joint Modeling for Generalized Linear Models with Covariates Subject to Detection Limits
Author(s): Paul Bernhardt*
Companies: Villanova University
Keywords: Multiple Imputation; Fully Conditional Specification; Detection Limits; Generalized Linear Models; Missing Covariates

Censoring due to detection limits is common in biomedical and environmental data sets. When the interest lies in inference for an outcome modeled via a generalized linear model, we propose several multiple imputation strategies. We develop a fully conditional specification approach that takes into account the informative nature of missingness and two joint modeling approaches for directly modeling the marginal distributions of the covariates subject to detection limits. We compare these strategies, as well as several previously proposed approaches, through extensive simulation studies that consider an array of underlying data generation models.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2018 program