D. M. Farewell
Ignorability for general longitudinal data
Farewell, D. M.; Huang, C.; Didelez, V.
Dr Chao Huang C.Huang@hull.ac.uk
Senior Lecturer in Statistics
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|
|Publisher||Oxford University Press (OUP)|
|Peer Reviewed||Peer Reviewed|
|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|
© 2017 Biometrika Trust
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