title: Doubly robust estimation and causal inference in longitudinal studies with dropout and truncation by death.
abstract: Motivated by aging research, we propose an estimator of the effect of a time-varying exposure on an outcome in longitudinal studies with dropout and truncation by death. We use an inverse-probability weighted (IPW) estimator to derive a doubly robust augmented inverse-probability weighted (AIPW) estimator. IPW estimation involves weights for the exposure mechanism, dropout, and mortality; AIPW estimation additionally involves estimating data-generating models via regression. We demonstrate that the estimators identify a causal contrast that is a function of principal strata effects under a set of assumptions. Simulations show that AIPW estimation is unbiased when weights or outcome regressions are correct, and that AIPW estimation is more efficient than IPW estimation when all models are correct. We apply the method to a study of vitamin D and gait speed among older adults.
authors.affiliation: Department of Epidemiology and Public Health, University of Maryland 660 West Redwood Street, Baltimore, MD 21201, USA mshardel@epi.umaryland.edu.
authors.forename: Michelle
authors.initials: M
authors.lastname: Shardell
authors.affiliation: Department of Physical Therapy, University of Delaware 303 McKinly Lab, Newark, DE 19716, USA.
authors.forename: Gregory E
authors.initials: GE
authors.lastname: Hicks
authors.affiliation: National Institute on Aging, 3001 S Hanover Street, Baltimore, MD 21225, USA.
authors.forename: Luigi
authors.initials: L
authors.lastname: Ferrucci
mesh_terms: D015982:Bias; D003627:Data Interpretation, Statistical; D006801:Humans; D009026:Mortality; D017063:Outcome Assessment, Health Care / Q000379:methods*; D010352:Patient Dropouts
keywords: Causal inference; Longitudinal data analysis; Missing data; Observational studies