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The principal focus of Dr. Robins’ research has been the development of analytic methods appropriate for drawing causal inferences from complex observational and randomized studies with time-varying exposures or treatments. The new methods are to a large extent based on the estimation of the parameters of a new class of causal models – the structural nested models – using a new class of estimators – the G estimators. The usual approach to the estimation of the effect of a time-varying treatment or exposure on time to disease is to model the hazard incidence of failure at time t as a function of past treatment history using a time-dependent Cox proportional hazards model. Dr. Robins has shown the usual approach may be biased whether or not further adjusts for past confounder history in the analysis when:
(A1) there exists a time-dependent risk factor for or predictor of the event of interest that also predicts subsequent treatment, and (A2) past treatment history predicts subsequent risk factor level.
Conditions (A1) and (A2) will be true whenever there are time-dependent covariates that are simultaneously confounders and intermediate variables.
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American journal of epidemiologyno. 4 (2024): 563-576
Journal of the Royal Statistical Society Series A: Statistics in Society (2024)
CoRR (2024)
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JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY (2024)
Guilin Li,Hanna Gerlovin, Michael J. Figueroa Muniz,Jessica K. Wise, Arin L. Madenci,James M. Robins,Mihaela Aslan,Kelly Cho,John Michael Gaziano,Marc Lipsitch,Juan P. Casas,Miguel A. Hernan,
EPIDEMIOLOGYno. 2 (2024): 137-149
arXiv (Cornell University)pp.105500-105500, (2023)
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