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Ignorability for general longitudinal data

Farewell, D. M.; Huang, C.; Didelez, V.

Authors

D. M. Farewell

V. Didelez



Abstract

Likelihood factors that can be disregarded for inference are termed ignorable. We demonstrate that close ties exist between ignorability and identification of causal effects by covariate adjustment. A graphical condition, stability, plays a role analogous to that of missingness at random, but is applicable to general longitudinal data. Our formulation of ignorability does not depend on any notion of missing data, so is appealing in situations where missing data may not actually exist. Several examples illustrate how stability may be assessed.

Journal Article Type Article
Publication Date 2017-06
Journal Biometrika
Print ISSN 0006-3444
Electronic ISSN 1464-3510
Publisher Oxford University Press (OUP)
Peer Reviewed Peer Reviewed
Volume 104
Issue 2
Pages 317-326
APA6 Citation Farewell, D. M., Huang, C., & Didelez, V. (2017). Ignorability for general longitudinal data. Biometrika, 104(2), 317-326. https://doi.org/10.1093/biomet/asx020
DOI https://doi.org/10.1093/biomet/asx020
Publisher URL https://academic.oup.com/biomet/article/104/2/317/3804413

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Copyright Statement
© 2017 Biometrika Trust
This is an Open Access article distributed under the terms of the Creative CommonsAttribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.





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