Tackling non‐ignorable dropout in the presence of time varying confounding. Issue 5 (4th May 2016)

• Record Type: Journal Article
• Authors: Doretti, Marco; Geneletti, Sara; Stanghellini, Elena
• Is Part Of: Journal of the Royal Statistical Society. Volume 65:Issue 5(2016:Nov.)
• Page Start: 775
• Page End: 795
• Publication Date: 2016-05-04
• Subjects: Causal inference -- Heckman correction -- Non‐ignorable dropout -- Selection models -- Time varying confounding; Statistics -- Periodicals; 519.5
• DOI: 10.1111/rssc.12154
• Languages: English
• ISSNs: 0035-9254

Abstract

Summary: We explore the sensitivity of time varying confounding adjusted estimates to different dropout mechanisms. We extend the Heckman correction to two time points and explore selection models to investigate situations where the dropout process is driven by unobserved variables and the outcome respectively. The analysis is embedded in a Bayesian framework which provides several advantages. These include fitting a hierarchical structure to processes that repeat over time and avoiding exclusion restrictions in the case of the Heckman correction. We adopt the decision theoretic approach to causal inference which makes explicit the no‐regime‐dropout dependence assumption. We apply our methods to data from the 'Counterweight programme' pilot: a UK protocol to address obesity in primary care. A simulation study is also implemented.