Structural Nested Models and G-estimation: The Partially Realized Promise
arXiv:1503.01589 · doi:10.1214/14-STS493
Abstract
Structural nested models (SNMs) and the associated method of G-estimation were first proposed by James Robins over two decades ago as approaches to modeling and estimating the joint effects of a sequence of treatments or exposures. The models and estimation methods have since been extended to dealing with a broader series of problems, and have considerable advantages over the other methods developed for estimating such joint effects. Despite these advantages, the application of these methods in applied research has been relatively infrequent; we view this as unfortunate. To remedy this, we provide an overview of the models and estimation methods as developed, primarily by Robins, over the years. We provide insight into their advantages over other methods, and consider some possible reasons for failure of the methods to be more broadly adopted, as well as possible remedies. Finally, we consider several extensions of the standard models and estimation methods.
Published in at http://dx.doi.org/10.1214/14-STS493 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)
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Cited by in corpus (9)
- Causal Etiology of the Research of James M. Robins
- Within-Person Variability Score-Based Causal Inference: A Two-Step Estimation for Joint Effects of Time-Varying Treatments
- Upstream Causes of Downstream Effects
- Semiparametric Estimation of Treatment Effects in Randomized Experiments
- Robust Inference for Mediated Effects in Partially Linear Models
- Risk-based decision making: estimands for sequential prediction under interventions
- Penalized G-estimation for effect modifier selection in a structural nested mean model for repeated outcomes
- Sensitivity Analysis of G-estimators to Invalid Instrumental Variables
- Counterfactual Prediction Under Selective Confounding