Structural Nested Mean Models for Assessing Time‐Varying Effect Moderation

  title={Structural Nested Mean Models for Assessing Time‐Varying Effect Moderation},
  author={Daniel Almirall and Thomas R. Ten Have and Susan A. Murphy},
Summary This article considers the problem of assessing causal effect moderation in longitudinal settings in which treatment (or exposure) is time varying and so are the covariates said to moderate its effect. Intermediate causal effects that describe time‐varying causal effects of treatment conditional on past covariate history are introduced and considered as part of Robins' structural nested mean model. Two estimators of the intermediate causal effects, and their standard errors, are… 
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The structural nested mean model makes no direct assumptions on selected treatment compliance levels and placebo prognosis but relies on the randomization assumption and a parametric form for causal effects, which can be seen as a regression model for unpaired data, where pre- and post-randomization covariables are treated differently.
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The control of confounding by intermediate variables.
  • J. Robins
  • Mathematics
    Statistics in medicine
  • 1989
An estimator is proposed, the extended standardized risk difference, that provides control for confounding by a covariate that is simultaneously a confounder and an intermediate variable on the causal pathway from exposure to disease.