Estimation and extrapolation of optimal treatment and testing strategies

By James M. Robins, Liliana Orellana, Andrea Rotnitzky. 2008

251 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

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Citations in the corpus

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).”
PERFORMANCE GUARANTEES FOR INDIVIDUALIZED TREATMENT RULES (2011, methodology): “of ITRs is desired, empirical versions of the Value can be used to construct estimators [22, 26].”
Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available. (2016, background): “…and common biases (41), facilitates a systematic methodologic evaluation of observational studies (42, 43) and the transportability of their estimates (44 – 46), and may help explain between-studies differences (47) as different observational analyses may be emulating different target trials.”
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).”
The International Journal of Biostatistics (2017, unknown): “…treatment regimens were developed and proposed in van der Laan (2006), van der Laan and Petersen (2007) and, simultaneously and independently, in Robins et al. (2008). van der Laan and Petersen (2007) also includes a data analysis application of these models to assess the mean outcome under a…”
Interactive Q-learning for Probabilities and Quantiles (2014, background): “arginal structural models (MSMs) are one type of direct-search estimators (Robins, 2000; van der Laan et al., 2005; van der Laan, 2006; van der Laan and Petersen, 2007; Bembom and van der Laan, 2008; Robins et al., 2008; Orellana et al., 2010; Petersen et al., 2014). MSMs are best-suited to problems”
A Robust Method for Estimating Optimal Treatment Regimes (2012, methodology): “Finally, as shown by Robins et al. (1994), the second term in (3) “augments” the estimator IPWE (η) so as to increase asymptotic efficiency; if π(X ; γ) is correctly specified, then the efficient estimator of form (3) is obtained when the regression model is also correct.”
Q- and A-learning Methods for Estimating Optimal Dynamic Treatment Regimes (2012, background): “Related work includes Thall, Millikan, and Sung (2000), Thall, Sung, and Etsey (2002), Robins (2004), Moodie, Richardson, and Stephens (2007), Thall et al. (2007), Robins, Orellana, and Rotnitzky (2008), Almirall, Ten Have, and Murphy (2010) and Orellana, Rotnitzky, and Robins (2010).”
Constructing Dynamic Treatment Regimes in Infinite-Horizon Settings (2014, unknown): “riates required to model the visit process. The latter, however, is more dicult and results in non-ignorable missing data because we do not have information about those patients who did not show up. Robins et al. (2008) discusses the issue of the random visit process in detail. ACKNOWLEDGEMENT This ”
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).”
Q-LEARNING WITH CENSORED DATA (2012, methodology): “Advantages and disadvantages of this approach compared to the backward recursion approach mentioned above are discussed in Robins, Orellana and Rotnitzky (2008), Section 5.”