Intervening on risk factors for coronary heart disease: an application of the parametric g-formula.

By S. Taubman, James M. Robins, M. Mittleman, Miguel A. Hern'an. 2009

352 citations recorded by the source corpus.

Subfields: Public Health, Environmental and Occupational Health, Statistics and Probability

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Citations in the corpus

G-Net: A Deep Learning Approach to G-computation for Counterfactual Outcome Prediction Under Dynamic Treatment Regimes (2020, methodology): “Illustrative applications of the general approach are provided in [6, 16], and summaries of g-computation (and other “g-methods” for estimating time-varying treatment effects) can be found in [2, 3].”
Gformula: Estimating Causal Effects in the Presence of Time-Varying Confounding or Mediation using the G-Computation Formula (2011, positive): “However, the g-computation procedure has some clear advantages over these methods: it can more easily deal with complex multivariate (or joint) interventions, and can compare the hypothetical interventions with the observational regime, which can be important in informing policy (Taubman et al., 200”
Causal mediation analysis for stochastic interventions (2019, neutral): “…was first considered by Stock (1989) and has been the subject of recent study (Robins et al., 2004; Didelez et al., 2006; Tian, 2008; Pearl, 2009; Taubman et al., 2009; Stitelman et al., 2010; Dı́az and van der Laan, 2013; Dudı́k et al., 2014; Haneuse and Rotnitzky, 2013; Young et al., 2014).”
A Bayesian Nonparametic Approach for Estimating Individualized Treatment-Response Curves (2016, background): “When the data are collected at fixed regular intervals, discrete-time approaches that maintain estimates only at specific points-in-time are adequate (e.g. Taubman et al. (2009)).”
A Quantile-Based g-Computation Approach to Addressing the Effects of Exposure Mixtures (2019, neutral): “…has been described in detail by multiple authors in both frequentist and Bayesian settings (Keil et al. 2014, 2018a; Snowden et al. 2011; Taubman et al. 2009; Westreich et al. 2012; Young et al. 2011), we note here that it is a generalization of standardization that uses the law of…”
Reliable Decision Support using Counterfactual Models (2017, methodology): “Potential outcomes have also been used to estimate the causal effect of a sequence of actions in discrete time on a final outcome (e.g. Robins 1986, Robins and Hernán 2009, Taubman et al. 2009).”
Identifying the consequences of dynamic treatment strategies: A decision-theoretic overview (2010, methodology): “Hence we find only a few instances where G-computation has been used for practical data analysis (Robins, Greenland and Hu, 1999; Taubman et al., 2009).”
Comparative effectiveness of dynamic treatment regimes: an application of the parametric g-formula (2011, methodology): “2, the estimation algorithm described here extends that described in Taubman et al. [ 26 ] and Robins et al. [25 ]t o allow for incorporation of ap rioriknowledge of the observed data densities conditional on certain histories.”