Identifying and estimating effects of sustained interventions under parallel trends assumptions

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Creator
  • Renson A.
    • Other Affiliation: New York University Grossman School of Medicine
  • Hudgens M.G.
    • Affiliation: Gillings School of Global Public Health, Department of Biostatistics
  • Keil A.P.
    • Affiliation: Gillings School of Global Public Health, Department of Epidemiology
  • Zivich P.N.
    • Affiliation: Gillings School of Global Public Health, Department of Epidemiology
  • Aiello A.E.
    • Other Affiliation: Columbia University
Abstract
  • Many research questions in public health and medicine concern sustained interventions in populations defined by substantive priorities. Existing methods to answer such questions typically require a measured covariate set sufficient to control confounding, which can be questionable in observational studies. Differences-in-differences rely instead on the parallel trends assumption, allowing for some types of time-invariant unmeasured confounding. However, most existing difference-in-differences implementations are limited to point treatments in restricted subpopulations. We derive identification results for population effects of sustained treatments under parallel trends assumptions. In particular, in settings where all individuals begin follow-up with exposure status consistent with the treatment plan of interest but may deviate at later times, a version of Robins' g-formula identifies the intervention-specific mean under stable unit treatment value assumption, positivity, and parallel trends. We develop consistent asymptotically normal estimators based on inverse-probability weighting, outcome regression, and a double robust estimator based on targeted maximum likelihood. Simulation studies confirm theoretical results and support the use of the proposed estimators at realistic sample sizes. As an example, the methods are used to estimate the effect of a hypothetical federal stay-at-home order on all-cause mortality during the COVID-19 pandemic in spring 2020 in the United States.
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Resource type
  • Article
Rights statement
  • In Copyright
Journal title
  • Biometrics
Journal volume
  • 79
Journal issue
  • 4
Page start
  • 2998
Page end
  • 3009
Language
  • English
Funder
  • National Institutes of Health, NIH, (P2C‐HD050924, R01 AI085073, T32‐AI007001, T32‐HD091058‐02)
ISSN
  • 0006-341X
Publisher
  • John Wiley and Sons Inc

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