Dynamic regime marginal structural mean models for estimation of optimal dynamic treatment regimes, part I: Main content
Dynamic treatment regimes are set rules for sequential decision making based on patient covariate history. Observational studies are well suited for the investigation of the effects of dynamic treatment regimes because of the variability in treatment decisions found in them. This variability exists...
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todo:paper_15574679_v6_n2_p_Orellana2023-10-03T16:25:43Z Dynamic regime marginal structural mean models for estimation of optimal dynamic treatment regimes, part I: Main content Orellana, L. Rotnitzky, A. Robins, J.M. Causality Double-robust Dynamic treatment regime Inverse probability weighted Marginal structural model Optimal treatment regime article disease registry mathematical model medical decision making patient information probability register statistical analysis treatment planning algorithm clinical trial (topic) longitudinal study methodology probability statistical model statistics marginal structural model mathematical model statistical analysis decision making Algorithms Clinical Trials as Topic Longitudinal Studies Models, Statistical Probability Research Design Decision Making Dynamic treatment regimes are set rules for sequential decision making based on patient covariate history. Observational studies are well suited for the investigation of the effects of dynamic treatment regimes because of the variability in treatment decisions found in them. This variability exists because different physicians make different decisions in the face of similar patient histories. In this article we describe an approach to estimate the optimal dynamic treatment regime among a set of enforceable regimes. This set is comprised by regimes defined by simple rules based on a subset of past information. The regimes in the set are indexed by a Euclidean vector. The optimal regime is the one that maximizes the expected counterfactual utility over all regimes in the set. We discuss assumptions under which it is possible to identify the optimal regime from observational longitudinal data. Murphy et al. (2001) developed efficient augmented inverse probability weighted estimators of the expected utility of one fixed regime. Our methods are based on an extension of the marginal structural mean model of Robins (1998, 1999) which incorporate the estimation ideas of Murphy et al. (2001). Our models, which we call dynamic regime marginal structural mean models, are specially suitable for estimating the optimal treatment regime in a moderately small class of enforceable regimes of interest. We consider both parametric and semiparametric dynamic regime marginal structural models. We discuss locally efficient, double-robust estimation of the model parameters and of the index of the optimal treatment regime in the set. In a companion paper in this issue of the journal we provide proofs of the main results. © 2010 The Berkeley Electronic Press. All rights reserved. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_15574679_v6_n2_p_Orellana |
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Universidad de Buenos Aires |
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I-28 |
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R-134 |
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Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
Causality Double-robust Dynamic treatment regime Inverse probability weighted Marginal structural model Optimal treatment regime article disease registry mathematical model medical decision making patient information probability register statistical analysis treatment planning algorithm clinical trial (topic) longitudinal study methodology probability statistical model statistics marginal structural model mathematical model statistical analysis decision making Algorithms Clinical Trials as Topic Longitudinal Studies Models, Statistical Probability Research Design Decision Making |
| spellingShingle |
Causality Double-robust Dynamic treatment regime Inverse probability weighted Marginal structural model Optimal treatment regime article disease registry mathematical model medical decision making patient information probability register statistical analysis treatment planning algorithm clinical trial (topic) longitudinal study methodology probability statistical model statistics marginal structural model mathematical model statistical analysis decision making Algorithms Clinical Trials as Topic Longitudinal Studies Models, Statistical Probability Research Design Decision Making Orellana, L. Rotnitzky, A. Robins, J.M. Dynamic regime marginal structural mean models for estimation of optimal dynamic treatment regimes, part I: Main content |
| topic_facet |
Causality Double-robust Dynamic treatment regime Inverse probability weighted Marginal structural model Optimal treatment regime article disease registry mathematical model medical decision making patient information probability register statistical analysis treatment planning algorithm clinical trial (topic) longitudinal study methodology probability statistical model statistics marginal structural model mathematical model statistical analysis decision making Algorithms Clinical Trials as Topic Longitudinal Studies Models, Statistical Probability Research Design Decision Making |
| description |
Dynamic treatment regimes are set rules for sequential decision making based on patient covariate history. Observational studies are well suited for the investigation of the effects of dynamic treatment regimes because of the variability in treatment decisions found in them. This variability exists because different physicians make different decisions in the face of similar patient histories. In this article we describe an approach to estimate the optimal dynamic treatment regime among a set of enforceable regimes. This set is comprised by regimes defined by simple rules based on a subset of past information. The regimes in the set are indexed by a Euclidean vector. The optimal regime is the one that maximizes the expected counterfactual utility over all regimes in the set. We discuss assumptions under which it is possible to identify the optimal regime from observational longitudinal data. Murphy et al. (2001) developed efficient augmented inverse probability weighted estimators of the expected utility of one fixed regime. Our methods are based on an extension of the marginal structural mean model of Robins (1998, 1999) which incorporate the estimation ideas of Murphy et al. (2001). Our models, which we call dynamic regime marginal structural mean models, are specially suitable for estimating the optimal treatment regime in a moderately small class of enforceable regimes of interest. We consider both parametric and semiparametric dynamic regime marginal structural models. We discuss locally efficient, double-robust estimation of the model parameters and of the index of the optimal treatment regime in the set. In a companion paper in this issue of the journal we provide proofs of the main results. © 2010 The Berkeley Electronic Press. All rights reserved. |
| format |
JOUR |
| author |
Orellana, L. Rotnitzky, A. Robins, J.M. |
| author_facet |
Orellana, L. Rotnitzky, A. Robins, J.M. |
| author_sort |
Orellana, L. |
| title |
Dynamic regime marginal structural mean models for estimation of optimal dynamic treatment regimes, part I: Main content |
| title_short |
Dynamic regime marginal structural mean models for estimation of optimal dynamic treatment regimes, part I: Main content |
| title_full |
Dynamic regime marginal structural mean models for estimation of optimal dynamic treatment regimes, part I: Main content |
| title_fullStr |
Dynamic regime marginal structural mean models for estimation of optimal dynamic treatment regimes, part I: Main content |
| title_full_unstemmed |
Dynamic regime marginal structural mean models for estimation of optimal dynamic treatment regimes, part I: Main content |
| title_sort |
dynamic regime marginal structural mean models for estimation of optimal dynamic treatment regimes, part i: main content |
| url |
http://hdl.handle.net/20.500.12110/paper_15574679_v6_n2_p_Orellana |
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