Estimation of Survival Distributions of Treatment Policies in Two‐Stage Randomization Designs in Clinical Trials

By J. Lunceford, Marie Davidian, Anastasios A. Tsiatis. 2002

145 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

Citations in the corpus

Reinforcement Learning in Healthcare: A Survey (2019, background): “Dynamic treatment regimes (DTRs) [72], [73], alternatively named as dynamic treatment policies [74], adap-”
PERFORMANCE GUARANTEES FOR INDIVIDUALIZED TREATMENT RULES (2011, background): “There is an abundance of statistical literature in this area [17, 20, 21, 27, 29, 30, 34, 35].”
Near-Optimal Reinforcement Learning in Dynamic Treatment Regimes (2019, methodology): “These decision rules are alternatively known as adaptive treatment strategies [12, 13, 19, 33, 34] or treatment policies [16, 37, 38].”
A Bayesian Nonparametic Approach for Estimating Individualized Treatment-Response Curves (2016, background): “…models (van der Laan and Petersen, 2007), doubly robust learning (Tsiatis, 2007; Zhao et al., 2015) with applications to longitudinal data analysis (Hernán et al., 2000), survival analysis (Lunceford et al., 2002), and adaptive treatment selections in clinical trials (Murphy et al., 2007a,b).”
Penalized Q-Learning for Dynamic Treatment Regimens (2015, unknown): “Other related literature includes likelihood-based methods (Thall et al., 2000, 2002, 2007) and semiparametric methods (Murphy, 2003; Robins, 2004; Lunceford et al., 2002; Wahed and Tsiatis, 2004, 2006; Moodie et al., 2009).”
Q-LEARNING WITH CENSORED DATA (2012, methodology): “This general issue is also related to the analysis of two-stage randomized trials involving right-censored data studied in a series of papers including Lunceford, Davidian and Tsiatis (2002), Wahed and Tsiatis (2006), Wahed (2009), Miyahara and Wahed (2010).”
Estimating the optimal dynamic antipsychotic treatment regime: Evidence from the sequential multiple assignment randomized CATIE Schizophrenia Study (2012, methodology): “treatment regimens that are not tailored to evolving patient characteristics— marginal structural models (MSMs) are increasingly being applied to the problem of selecting or indeed estimating the optimal dynamic treatment regime (Lunceford et al., 2002; Wahed and Tsiatis, 2004; Hernán et al., 2006; ”
Robust Hybrid Learning for Estimating Personalized Dynamic Treatment Regimens (2016, background): “They are also known as adaptive treatment strategies (Lavori & Dawson, 2000), multi-stage treatment strategies (Thall, Sung, & Estey, 2002; Thall & Wathen, 2005) and treatment policies (Lunceford, Davidian, & Tsiatis, 2002; Wahed & Tsiatis, 2004, 2006).”
Dynamic treatment regimes (2009, methodology): “Inferential methods have been discussed in Robins (2004), Moodie and Richardson (2007) and Chakraborty and Murphy (2009).”
Adaptive Interventions Treatment Modelling and Regimen Optimization Using Sequential Multiple Assignment Randomized Trials (Smart) and Q-Learning (2018, background): “DTRs are also known as treatment strategies [5, 29-31], adaptive treatment strategies [32, 33] or treatment policy [34-36]and can be understood as the system that supports a decision maker for making clinical or treatment decisions in medical scenario.”