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Estimating treatment effects under untestable assumptions with nonignorable missing data

Gomes, M; Kenward, MG; Grieve, R; Carpenter, J; (2020) Estimating treatment effects under untestable assumptions with nonignorable missing data. Statistics in Medicine , 39 (11) pp. 1658-1674. 10.1002/sim.8504. Green open access

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Abstract

Nonignorable missing data poses key challenges for estimating treatment effects because the substantive model may not be identifiable without imposing further assumptions. For example, the Heckman selection model has been widely used for handling nonignorable missing data but requires the study to make correct assumptions, both about the joint distribution of the missingness and outcome and that there is a valid exclusion restriction. Recent studies have revisited how alternative selection model approaches, for example estimated by multiple imputation (MI) and maximum likelihood, relate to Heckman‐type approaches in addressing the first hurdle. However, the extent to which these different selection models rely on the exclusion restriction assumption with nonignorable missing data is unclear. Motivated by an interventional study (REFLUX) with nonignorable missing outcome data in half of the sample, this article critically examines the role of the exclusion restriction in Heckman, MI, and full‐likelihood selection models when addressing nonignorability. We explore the implications of the different methodological choices concerning the exclusion restriction for relative bias and root‐mean‐squared error in estimating treatment effects. We find that the relative performance of the methods differs in practically important ways according to the relevance and strength of the exclusion restriction. The full‐likelihood approach is less sensitive to alternative assumptions about the exclusion restriction than Heckman‐type models and appears an appropriate method for handling nonignorable missing data. We illustrate the implications of method choice for inference in the REFLUX study, which evaluates the effect of laparoscopic surgery on long‐term quality of life for patients with gastro‐oseophageal reflux disease.

Type: Article
Title: Estimating treatment effects under untestable assumptions with nonignorable missing data
Open access status: An open access version is available from UCL Discovery
DOI: 10.1002/sim.8504
Publisher version: https://doi.org/10.1002/sim.8504
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: average treatment effects, full‐information maximum likelihood, Heckman model, missing not at random, multiple imputation, selection models
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
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology > MRC Clinical Trials Unit at UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Epidemiology and Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Epidemiology and Health > Applied Health Research
URI: https://discovery.ucl.ac.uk/id/eprint/10096962
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