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Ignorability for general longitudinal data

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  • D. M. Farewell
  • C. Huang
  • V. Didelez

Abstract

SummaryLikelihood 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.

Suggested Citation

  • D. M. Farewell & C. Huang & V. Didelez, 2017. "Ignorability for general longitudinal data," Biometrika, Biometrika Trust, vol. 104(2), pages 317-326.
    • Handle: RePEc:oup:biomet:v:104:y:2017:i:2:p:317-326.
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    File URL: http://hdl.handle.net/10.1093/biomet/asx020
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    References listed on IDEAS

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    1. Geert Molenberghs & Caroline Beunckens & Cristina Sotto & Michael G. Kenward, 2008. "Every missingness not at random model has a missingness at random counterpart with equal fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(2), pages 371-388, April.
    2. Fabrizia Mealli & Donald B. Rubin, 2015. "Clarifying missing at random and related definitions, and implications when coupled with exchangeability," Biometrika, Biometrika Trust, vol. 102(4), pages 995-1000.
    3. Liuquan Sun & Xinyuan Song & Jie Zhou & Lei Liu, 2012. "Joint Analysis of Longitudinal Data With Informative Observation Times and a Dependent Terminal Event," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 688-700, June.
    4. Lindsey, J. K., 1999. "Models for Repeated Measurements," OUP Catalogue, Oxford University Press, edition 2, number 9780198505594.
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    Cited by:

    1. D M Farewell & R M Daniel & S R Seaman, 2022. "Missing at random: a stochastic process perspective [Contribution to the discussion of ‘Longitudinal data with dropout: Objectives, assumptions and a proposal’ by P. J. Diggle, D. Farewell and R. H," Biometrika, Biometrika Trust, vol. 109(1), pages 227-241.
    2. Daniel Commenges, 2019. "Dealing with death when studying disease or physiological marker: the stochastic system approach to causality," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(3), pages 381-405, July.

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