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Ignorability for general longitudinal data

Farewell, Daniel, Huang, Chao and Didelez, V. 2017. Ignorability for general longitudinal data. Biometrika 104 (2) , pp. 317-326. 10.1093/biomet/asx020

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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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Medicine
Publisher: Oxford University Press
ISSN: 0006-3444
Funders: MRC
Date of First Compliant Deposit: 28 April 2017
Date of Acceptance: 2 February 2017
Last Modified: 19 Sep 2019 10:51
URI: http://orca.cf.ac.uk/id/eprint/100178

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