A Robust Method for Estimating Optimal Treatment Regimes

By Baqun Zhang, Anastasios A. Tsiatis, Eric B. Laber, Marie Davidian. 2012

423 citations recorded by the source corpus.

Summary A treatment regime is a rule that assigns a treatment, among a set of possible treatments, to a patient as a function of his/her observed characteristics, hence “personalizing” treatment to the patient. The goal is to identify the optimal treatment regime that, if followed by the entire population of patients, would lead to the best outcome on average. Given data from a clinical trial or observational study, for a single treatment decision, the optimal regime can be found by assuming a regression model for the expected outcome conditional on treatment and covariates, where, for a given set of covariates, the optimal treatment is the one that yields the most favorable expected outcome. However, treatment assignment via such a regime is suspect if the regression model is incorrectly specified. Recognizing that, even if misspecified, such a regression model defines a class of regimes, we instead consider finding the optimal regime within such a class by finding the regime that optimizes an estimator of overall population mean outcome. To take into account possible confounding in an observational study and to increase precision, we use a doubly robust augmented inverse probability weighted estimator for this purpose. Simulations and application to data from a breast cancer clinical trial demonstrate the performance of the method.

Subfields: Statistics and Probability

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Citations in the corpus

Deep Reinforcement Learning for Dynamic Treatment Regimes on Medical Registry Data (2017, background): “Methods to identify the optimal DTRs have recently received attentions in the statistical community [7], [8], [9], [10], [11], [12], [13], [14], and most of exsiting methods focused on data from randomized clinical trials.”
Estimating treatment effect heterogeneity in randomized program evaluation (2013, methodology): “Finally, it is of interest to consider how the proposed method can be applied to observational data [e.g., see Zhang et al. (2012) who develop a doubly robust estimator for optimal treatment regimes] and longitudinal data settings where the derivation of optimal dynamic treatment regimes is a…”
Using Decision Lists to Construct Interpretable and Parsimonious Treatment Regimes (2015, methodology): “Alternate expressions for R(π) exist (Zhang et al., 2012); however, estimators based on (1) possess a number of desirable properties (see below).”
On Optimal Treatment Regimes Selection for Mean Survival Time (2014, positive): “Recently, [6] proposed a robust estimation method for the mean response under a given treatment regime based on the inverse probability of propensity scores weighted (IPW) estimation and extended it to the augmented IPW estimation with the double robustness.”
Interactive Q-learning for Quantiles (2017, methodology): “Using the potential outcomes framework (Rubin 1974; Rosenbaum and Rubin 1983), Zhang et al. (2012c) developed methods for estimating quantiles of the potential outcomes from observational data.”
Estimation of Optimal Dynamic Treatment Regimes (2014, methodology): “Methods for estimating V(d), include inverse probability-weighting [9,14]; augmented inverse probability-weighting [10,13]; and marginal structural mean models [7].”
Doubly Robust Learning for Estimating Individualized Treatment with Censored Data (2014, neutral): “Alternative choices have been proposed for continuous outcomes (Zhang et al., 2012b; Rubin & van der Lann, 2012), which can be generalized to the censoring data setup.”
Interactive Q-learning for Probabilities and Quantiles (2014, methodology): “Using the potential outcomes framework (Rubin, 1974; Rosenbaum and Rubin, 1983), Zhang et al. (2012c) develop methods for estimating quantiles of the potential outcomes from observational data.”
Robust Hybrid Learning for Estimating Personalized Dynamic Treatment Regimens (2016, methodology): “Zhang et al. (2012) and Zhang et al. (2013) developed a method based on augmented inverse probability weighting for a single or multiple stage model.”
Estimating Optimal Treatment Regimes from a Classification Perspective (2012, methodology): “As an alternative to the AIPWE estimator, Zhang et al. (2012a) also discussed the inverse probability weighted estimator (IPWE) for E1Y .”
Learning Cost-Effective and Interpretable Treatment Regimes (2017, neutral): “Policy-search-based methods [28, 42, 41, 38, 39] search for a policy (a function which assigns treatments to individuals) within a pre-specified class of policies such that the resulting expected outcome is maximized across the population of interest.”
New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes (2015, methodology): “Since the proposed methods do not model the relationship between outcomes and DTRs, they may be more robust to model misspecification than statistical modeling alternatives such as Q-learning (Zhang et al. 2012a,b).”
Precision Medicine. (2019, methodology): “To mitigate misspecification, one can use a flexible class of models to estimate Q(x, a), e.g., trees (Taylor et al 2015; Zhang et al 2012b), boosting (Kang et al 2014), generalized additive models (Moodie et al 2014), or non-linear basis expansions (Qian and Murphy 2011).”
Estimating Dynamic Treatment Regimes in Mobile Health Using V-learning (2016, methodology): “Our approach, which we call V-learning, involves estimating the optimal policy among a prespeciﬁed class of policies (Zhang et al., 2012, 2013).”
Sparse concordance-assisted learning for optimal treatment decision (2018, methodology): “2 Estimate the threshold using the inverse probability weighted estimator (IPW) proposed by Zhang et al. (2012): β̂0 = argmax β0 1 n n ∑”
Incorporating Patient Preferences into Estimation of Optimal Individualized Treatment Rules (2017, neutral): “…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 International Biometric…”