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

Joseph G. Ibrahim, Stuart R. Lipsitz and Nicholas Horton

Journal of the Royal Statistical Society Series C, 2001, vol. 50, issue 3, 361-373

Abstract: 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.

Date: 2001
References: Add references at CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
https://doi.org/10.1111/1467-9876.00240

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssc:v:50:y:2001:i:3:p:361-373

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-9876

Access Statistics for this article

Journal of the Royal Statistical Society Series C is currently edited by R. Chandler and P. W. F. Smith

More articles in Journal of the Royal Statistical Society Series C from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().