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Canonical Causal Diagrams to Guide the Treatment of Missing Data in Epidemiological Studies

Moreno-Betancur, M; Lee, KJ; Leacy, FP; White, IR; Simpson, JA; Carlin, JB; (2018) Canonical Causal Diagrams to Guide the Treatment of Missing Data in Epidemiological Studies. American Journal of Epidemiology , 187 (12) pp. 2705-2715. 10.1093/aje/kwy173. Green open access

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Abstract

With incomplete data, the missing at random (MAR) assumption is widely understood to enable unbiased estimation with appropriate methods. The need to assess the plausibility of MAR and to perform sensitivity analyses considering missing not at random (MNAR) scenarios have been emphasized, but the practical difficulty of these tasks is rarely acknowledged. What MAR means with multivariable missingness is difficult to grasp, while in many MNAR scenarios unbiased estimation is possible using methods commonly associated with MAR. Directed acyclic graphs (DAGs) have been proposed as an alternative framework for specifying practically accessible assumptions beyond the MAR-MNAR dichotomy. However, there is currently no general algorithm for deciding how to handle the missing data given a specific DAG. We construct "canonical" DAGs capturing typical missingness mechanisms in epidemiological studies with incomplete exposure, outcome and confounders. For each DAG, we determine whether common target parameters are "recoverable", meaning that they can be expressed as functions of the observed data distribution and thus estimated consistently, or if sensitivity analyses are necessary. We investigate the performance of available case and multiple imputation procedures. Using the Longitudinal Study of Australian Children, we illustrate how our findings can guide the treatment of missing data in point-exposure studies.

Type: Article
Title: Canonical Causal Diagrams to Guide the Treatment of Missing Data in Epidemiological Studies
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1093/aje/kwy173
Publisher version: https://doi.org/10.1093/aje/kwy173
Language: English
Additional information: © The Author(s) 2018. This is an Open Access article distributed under the terms of the Creative Commons Attribution 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.
Keywords: Directed acyclic graphs, missing at random, missing data, missing not at random, multiple imputation, potential outcomes, recoverability, sensitivity analysis
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology
URI: https://discovery.ucl.ac.uk/id/eprint/10057724
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