Doubly robust estimation and causal inference in longitudinal studies with dropout and truncation by death

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) est...

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Bibliographic Details
Published in:Biostatistics (Oxford, England) England), 2015-01, Vol.16 (1), p.155-168
Main Authors: Shardell, Michelle, Hicks, Gregory E, Ferrucci, Luigi
Format: Article
Language:English
Subjects:
Bias
Data Interpretation, Statistical
Estimating techniques
Humans
Mortality
Older people
Outcome Assessment, Health Care - methods
Patient Dropouts
Probability distribution
Regression analysis
Simulation
Vitamin D
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Summary: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.
ISSN:1465-4644
1468-4357
DOI:10.1093/biostatistics/kxu032