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Causal inference with longitudinal outcomes and non‐ignorable dropout: estimating the effect of living alone on cognitive decline
Author(s) -
Josefsson Maria,
Luna Xavier,
Daniels Michael J.,
Nyberg Lars
Publication year - 2016
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/rssc.12110
Subject(s) - propensity score matching , causal inference , covariate , confounding , econometrics , inference , cognitive decline , matching (statistics) , dropout (neural networks) , psychology , cognition , statistics , computer science , mathematics , medicine , dementia , artificial intelligence , machine learning , disease , pathology , neuroscience
Summary We develop a model to estimate the causal effect of living arrangement (living alone versus living with someone) on cognitive decline based on a 15‐year prospective cohort study, where episodic memory function is measured every 5 years. One key feature of the model is the combination of propensity score matching to balance confounding variables between the two living arrangement groups—to reduce bias due to unbalanced covariates at baseline, with a pattern–mixture model for longitudinal data—to deal with non‐ignorable dropout. A fully Bayesian approach allows us to convey the uncertainty in the estimation of the propensity score and subsequent matching in the inference of the causal effect of interest. The analysis conducted adds to previous studies in the literature concerning the protective effect of living with someone, by proposing a modelling approach treating living arrangement as an exposure.