Dynamic Regime Marginal Structural Mean Models for Estimation of Optimal Dynamic Treatment Regimes, Part I: Main Content
By Liliana Orellana, Andrea Rotnitzky, James M. Robins. 2010
228 citations recorded by the source corpus.
Subfields: Statistics and Probability
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In Statistics and Probability
- Eric B. Laber
- Michael R. Kosorok
- Kristin A. Linn
- Leonard A. Stefanski
- Ying‐Qi Zhao
- Marie Davidian
- Anastasios A. Tsiatis
- Donglin Zeng
Citations in the corpus
- G-Net: A Deep Learning Approach to G-computation for Counterfactual Outcome Prediction Under Dynamic Treatment Regimes (2020, methodology): “G-methods include gcomputation [4–6], structural nested models [7, 8], and marginal structural models [9, 10].”
- Using Decision Lists to Construct Interpretable and Parsimonious Treatment Regimes (2015, background): “Examples include marginal structural mean models (Robins et al., 2008; Orellana et al., 2010); robust marginal mean models (Zhang et al., 2012); and outcomeweighted learning (Zhang et al., 2012; Zhao et al., 2012, 2015).”
- Introduction to SMART designs for the development of adaptive interventions: with application to weight loss research (2014, methodology): “Robins JM, Orellana L, Rotnitzky A. Estimation and extrapolation in optimal treatment and testing strategies.”
- Incorporating Patient Preferences into Estimation of Optimal Individualized Treatment Rules (2017, unknown): “…2011; Zhao et al., 2011; Goldberg and Kosorok, 2012; Moodie et al., 2012, 2014; Laber et al., 2014; Tian et al., 2014) or policy-search-based (Orellana et al., 2010; Zhang et al., 2012, 2013; Zhao et al., 2012, 2014, 2015; Laber and Zhao, 2015), though this division 18 © 2017, The…”
- Estimating Optimal Treatment Regimes from a Classification Perspective (2012, background): “Recently, there has been vigorous research on estimating optimal treatment regimes involving a single decision or a series of decisions based on data from clinical trials or observational studies (Murphy, 2003; Robins, 2004; Moodie et al., 2007; Robins et al., 2008; Brinkley et al., 2009; Zhao et al”
- A Robust Method for Estimating Optimal Treatment Regimes (2012, methodology): “…be involved (Murphy, 2003; Robins, 2004; Moodie, Richardson, and Stephens, 2007; Robins, Orellana, and Rotnitzky, 2008; Brinkley, Tsiatis, and Anstrom, 2009; Zhao, Kosorok, and Zeng, 2009; Henderson, Ansell, and Alshibani, 2010; Orellana, Rotnitzky, and Robins, 2010; Gunter, Zhu, and Murphy, 2011).”
- Robust estimation of optimal dynamic treatment regimes for sequential treatment decisions (2013, methodology): “See Rosthøj et al. (2006), Murphy et al. (2007), Zhao et al. (2009) and Henderson et al. (2010) for applications; related methods are discussed by Robins (2004), Moodie et al. (2007), Robins et al. (2008), Almirall et al. (2010) and Orellana et al. (2010).”
- Comparative effectiveness of dynamic treatment regimes: an application of the parametric g-formula (2011, methodology): “Cain et al. [3] applied inverse probability (IP) weighting of dynamic marginal structural models [2, 9, 16 , 17] to observational data from the HIV-CAUSAL Collaboration, and emulated a randomized clinical trial with multiple initiation strategies similar to the ones described above.”
- Interactive Q-learning for Probabilities and Quantiles (2014, unknown): “, 2010), and policy search methods (Orellana et al., 2010; Zhao et al., 2012; Zhang et al., 2012b,a, 2013).”
- New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes (2015, methodology): “Other approaches to direct maximization include robust estimation (Zhang et al. 2013) and the use of marginal structural mean models (Orellana, Rotnitzky, and Robins 2010).”
- TREE-BASED REINFORCEMENT LEARNING FOR ESTIMATING OPTIMAL DYNAMIC TREATMENT REGIMES (2018, background): “and ROBINS, J. M. (2001). Marginal structural models to estimate the joint causal effect of nonrandomized treatments. Journal of the American Statistical Association 96 440–448. HSER, Y.-I., ANGLIN, M. D., GRELLA, C., LONGSHORE, D. and PRENDERGAST, M. L. (1997). Drug treatment careers A conceptual f”
- Interactive Q-learning for Quantiles (2017, background): “…structural models (MSMs) are one type of direct-search estimators (Robins 2000; van der Laan, Petersen, and Joffe 2005; van der Laan 2006; van der Laan and Petersen 2007; Bembom and van der Laan 2008; Robins, Orellana, and Rotnitzky 2008; Orellana, Rotnitzky, and Robins 2010; Petersen et al. 2014).”