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Abstract Details

Activity Number: 300
Type: Contributed
Date/Time: Tuesday, July 31, 2012 : 8:30 AM to 10:20 AM
Sponsor: Biopharmaceutical Section
Abstract - #305893
Title: Statistical Methodology for Generalized Linear Models with Censored Covariates
Author(s): Paul Bernhardt*+ and Huixia Judy Wang and Daowen Zhang
Companies: North Carolina State University and North Carolina State University and North Carolina State University
Address: Department of Statistics, Raleigh, NC, 27695-8203, United States
Keywords: Biomarkers ; Censoring ; Detection Limits ; Generalized Linear Models ; Multiple Imputation

Censored observations are a common occurrence in biomedical datasets. Though censoring is commonly associated with time-to-event data, censored data also arise due to detection limits. Very little research has focused on statistical methodology when predictors are censored due to detection limits. We compare existing procedures and propose an improper multiple imputation method for analyzing datasets with censored predictors within the context of generalized linear models. We establish the consistency and asymptotic normality of the proposed multiple imputation estimator and provide a consistent variance estimator. Through an extensive simulation study, we demonstrate that the proposed multiple imputation method leads to consistent parameter estimates while several competing estimators are biased, more variable, or computationally intensive to obtain. We apply several methods to analyze the GenIMS dataset which has several biomarkers subject to censoring due to lower detection limits.

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