Using auxiliary data for parameter estimation with non-ignorably missing outcomes

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We propose a method for estimating parameters in generalized linear models when the outcome variable is missing for some subjects and the missing data mechanism is non-ignorable. We assume throughout that the covariates are fully observed. One possible method for estimating the parameters is maximum likelihood with a non-ignorable missing data model. However, caution must be used when fitting non-ignorable missing data models because certain parameters may be inestimable for some models. Instead of fitting a non-ignorable model, we propose the use of auxiliary information in a likelihood approach to reduce the bias, without having to specify a non-ignorable model. The method is applied to a mental health study.

Keywords: Auxiliary data; EM algorithm; Generalized linear models

Document Type: Original Article


Affiliations: 1: Harvard School of Public Health and Dana-Farber Cancer Institute, Boston, USA, 2: Harvard School of Public Health and Dana-Farber Cancer Institute, Boston, and Medical University of South Carolina, Charleston, USA, 3: Boston University, USA

Publication date: January 1, 2001

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