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

Michelle Shardell, Gregory E. Hicks, Luigi Ferrucci

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)155-168
Number of pages14
JournalBiostatistics
Volume16
Issue number1
DOIs
StatePublished - Jan 1 2015
Externally publishedYes

Keywords

  • Causal inference
  • Longitudinal data analysis
  • Missing data
  • Observational studies

ASJC Scopus subject areas

  • Medicine(all)
  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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