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Instrumental Variable Estimation of Marginal Structural Mean Models for Time-Varying Treatment

Author

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  • Haben Michael
  • Yifan Cui
  • Scott A. Lorch
  • Eric J. Tchetgen Tchetgen

Abstract

Robins introduced Marginal Structural Models (MSMs), a general class of counterfactual models for the joint effects of time-varying treatment regimes in complex longitudinal studies subject to time-varying confounding. In his work, identification of MSM parameters is established under a Sequential Randomization Assumption (SRA), which rules out unmeasured confounding of treatment assignment over time. We consider sufficient conditions for identification of the parameters of a subclass, Marginal Structural Mean Models (MSMMs), when sequential randomization fails to hold due to unmeasured confounding, using instead a time-varying instrumental variable. Our identification conditions require that no unobserved confounder predicts compliance type for the time-varying treatment. We describe a simple weighted estimator and examine its finite-sample properties in a simulation study. We apply the proposed estimator to examine the effect of delivery hospital type on neonatal survival probability. Supplementary materials for this article are available online.

Suggested Citation

  • Haben Michael & Yifan Cui & Scott A. Lorch & Eric J. Tchetgen Tchetgen, 2024. "Instrumental Variable Estimation of Marginal Structural Mean Models for Time-Varying Treatment," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 119(546), pages 1240-1251, April.
    • Handle: RePEc:taf:jnlasa:v:119:y:2024:i:546:p:1240-1251
    DOI: 10.1080/01621459.2023.2183131
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