Dynamic Regime Marginal Structural Mean Models for Estimation of Optimal Dynamic Treatment Regimes, Part I: Main Content
Open accessAbstract
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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The statistical foundation for the weighting-and-replication method is in Orellana, Rotnitzky, and Robins.
“The statistical foundation for the weightingand-replication method is found in the work by Orellana, Rotnitzky, and Robins [33,34,68].”
From Introduction to SMART designs for the development of adaptive interventions: with application to weight loss research (2014) · cites this paper as [33]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Optimization by Vector Space Methods
(1970) · Students Quarterly Journal · doi:10.1049/sqj.1970.0088 · Cited by 6,024 · see citing works
Estimation of Regression Coefficients When Some Regressors Are Not Always Observed
Robins, J. M., Rotnitzky, A. G., & Zhao, L. P. (1994) · Journal of the American Statistical Association · doi:10.1080/01621459.1994.10476818 · Cited by 2,803 · see citing works
Self-citationBayesian inference for causal effects: The role of randomization
Rubin, D. B. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344064 · Cited by 2,503 · see citing works
Large sample estimation and hypothesis testingPreprint
Newey, W. K., & McFadden, D. L. (1986) · RePEc: Research Papers in Economics · Cited by 2,192 · see citing works
Hernán, M. Á., Brumback, B., & Robins, J. M. (2000) · Epidemiology · doi:10.1097/00001648-200009000-00012 · Cited by 1,772 · see citing works
Self-citationOptimal Dynamic Treatment Regimes
Murphy, S. A. (2003) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/1467-9868.00389 · Cited by 988 · see citing works
Unified Methods for Censored Longitudinal Data and Causality
van der Laan, M. J., & Robins, J. M. (2003) · Springer series in statistics · doi:10.1007/978-0-387-21700-0 · Cited by 972 · see citing works
Self-citationSemiparametric Theory and Missing Data
Tsiatis, A. A. (2006) · Springer eBooks · doi:10.1007/0-387-37345-4 · Cited by 859 · see citing works
Rubin, D. B. (1990) · Statistical Science · doi:10.1214/ss/1177012032 · Cited by 665 · see citing works
Recovery of Information and Adjustment for Dependent Censoring Using Surrogate Markers
Robins, J. M., & Rotnitzky, A. G. (1992) · Birkhäuser Boston eBooks · doi:10.1007/978-1-4757-1229-2_14 · Cited by 646 · see citing works
Self-citationMarginal Structural Models versus Structural nested Models as Tools for Causal inference
Robins, J. M. (2000) · The IMA volumes in mathematics and its applications · doi:10.1007/978-1-4612-1284-3_2 · Cited by 557 · see citing works
Self-citationOptimal Structural Nested Models for Optimal Sequential Decisions
Robins, J. M. (2004) · Lecture notes in statistics · doi:10.1007/978-1-4419-9076-1_11 · Cited by 547 · see citing works
Self-citationMarginal Mean Models for Dynamic Regimes
Murphy, S. A., van der Laan, M. J., Robins, J. M., & Conduct Problems Prevention Research Group (2001) · Journal of the American Statistical Association · doi:10.1198/016214501753382327 · Cited by 393 · see citing works
Self-citationCoarsening at Random: Characterizations, Conjectures, Counter-Examples
Gill, R. D., van der Laan, M. J., & Robins, J. M. (1997) · Lecture notes in statistics · doi:10.1007/978-1-4684-6316-3_14 · Cited by 208 · see citing works
Self-citationEstimation and extrapolation of optimal treatment and testing strategies
Robins, J., Orellana, L., & Rotnitzky, A. (2008) · Statistics in Medicine · doi:10.1002/sim.3301 · Cited by 208 · see citing works
Self-citationRobins, J. M. (1987) · Computers & Mathematics with Applications · doi:10.1016/0898-1221(87)90238-0 · Cited by 207 · see citing works
Self-citationRosenblum, M. (2011) · Springer series in statistics · doi:10.1007/978-1-4419-9782-1_9 · Cited by 185 · see citing works
Causal Effect Models for Realistic Individualized Treatment and Intention to Treat Rules
van der Laan, M. J., & Petersen, M. L. (2007) · The International Journal of Biostatistics · doi:10.2202/1557-4679.1022 · Cited by 175 · see citing works
Robins, J. M. (1987) · Computers & Mathematics with Applications · doi:10.1016/0898-1221(87)90237-9 · Cited by 91 · see citing works
Self-citationOn Profile Likelihood: Comment
Robins, J. M., Rotnitzky, A. G., & van der Laan, M. J. (2000) · Journal of the American Statistical Association · doi:10.2307/2669391 · Cited by 69 · see citing works
Self-citationOrellana, L., Rotnitzky, A., & Robins, J. M. (2010) · The International Journal of Biostatistics · doi:10.2202/1557-4679.1242 · Cited by 53 · see citing works
Self-citationBembom, O., & van der Laan, M. J. (2008) · Statistics in Medicine · doi:10.1002/sim.3268 · Cited by 35 · see citing works
Flagged first, then by citations
Research works printed but not linked: 9
- Gill, R.D, van der Laan, M.J. and Robins J.M. (1997). Coarsening at ran- dom: characterizations, conjectures and counterexamples. Proceedings of the First Seattle Symposium on Survival Analysis, pp. 255-294.
- The International Journal of Biostatistics, Vol. 6 [2010], Iss. 2, Art. 8 DOI: 10.2202/1557-4679.1200 Brought to you by | Deakin University Authenticated Download Date | 2/17/15 11:40 AM
- Luenberger, D.G. (1969). Optimization by vector spaces. Wiley, New York.
- Orellana, L. and Rotnitzky, A. (2007). Estimation of the e¤ect of dynamic treatment regimes under ‡e x i b l e dynamic visit regimes. Liliana Orellana's Harvard. University, Dep of Biostatistics, Ph.D. Thesis, Chapter 2.
- Robins, J. M. (1989). The analysis of randomized and nonrandomized AIDS treatment trials using a new approach to causal inference in longitudinal studies. In Health Service Research Methodology: a Focus on AIDS (eds L. Sechrest, H. Freeman and A. Mulley), pp. 113-159. US Public Health Service.
- Eds.: D. G. Ostrow & R. Kessler. Plenum Publishing, New York, pp. 213- 290.
- Robins, J.M. (1997). Causal inference from complex longitudinal data. Latent Variable Modeling and Applications to Causality. Lecture Notes in Statis- tics (120), 69-117, (eds: M. Berkane). Springer-Verlag, Inc, New York.
- Robins, J.M., Rotnitzky A and van der Laan M. (2000). Comment on "On Pro…le Likelihood" by S.A. Murphy and A.W.van der Vaart. Journal of the American Statistical Association -Theory and Methods, 95(450):431- 435.
- Rubin, D.B. (1978). Bayesian inference for causal e¤ects: The role of random- ization. The Annals of Statistics, 6, 34-58.
Cited by
Sorted by citations
Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available: Table 1.
Hernán, M. A., & Robins, J. M. (2016) · American Journal of Epidemiology · doi:10.1093/aje/kwv254 · Cited by 3,115 · see citing works
Self-citationAlmirall, D., Nahum-Shani, I., Sherwood, N. E., & Murphy, S. A. (2014) · Translational Behavioral Medicine · doi:10.1007/s13142-014-0265-0 · Cited by 453 · see citing works
Almirall, D., Compton, S. N., Gunlicks‐Stoessel, M., Duan, N., & Murphy, S. A. (2012) · Statistics in Medicine · doi:10.1002/sim.4512 · Cited by 223 · see citing works
Causal Inference About the Effects of Interventions From Observational Studies in Medical Journals
Dahabreh, I. J., & Bibbins-Domingo, K. (2024) · JAMA · doi:10.1001/jama.2024.7741 · Cited by 242 · see citing works
Wang, L., Rotnitzky, A., Lin, X., Millikan, R. E., & Thall, P. F. (2012) · Journal of the American Statistical Association · doi:10.1080/01621459.2011.641416 · Cited by 108 · see citing works
Self-citationOrellana, L., Rotnitzky, A., & Robins, J. M. (2010) · The International Journal of Biostatistics · doi:10.2202/1557-4679.1242 · Cited by 53 · see citing works
Self-citationUnderstanding Marginal Structural Models for Time-Varying Exposures: Pitfalls and Tips
Shinozaki, T., & Suzuki, E. (2020) · Journal of Epidemiology · doi:10.2188/jea.je20200226 · Cited by 44 · see citing works
- Authors
- Orellana, L., Rotnitzky, A., & Robins, J. M.
- Affiliations
- Universidad de Buenos Aires (AR)
- Harvard University (US)
- Venue
- The International Journal of Biostatistics
- Publisher
- Walter de Gruyter GmbH
- Published
- 2010-03-03
- Volume
- 6
- Issue
- 2
- Pages
- Article 8-Article 8
- ISSN
- 1557-4679 (ISSN-L)
- OpenAlex
- W2026904901
- Cited by
- 202
- References
- 22
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 14 · 2023: 17 · 2022: 10 · 2021: 8 · 2020: 16 · 2019: 12 · 2018: 18 · 2017: 13 · 2016: 20 · 2015: 12 · 2014: 21 · 2013: 11 · 2012: 14
- FWCI
- 8.99
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- The International Journal of Biostatistics (publishedVersion)
- PubMed (publishedVersion)
- Deakin Research Online (Deakin University) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Deakin Research Online (Deakin University)
- Funders
- National Institutes of Health
- National Institute of General Medical Sciences (R01GM048704, R01-GM48704)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- dynamic treatment regimes, optimal treatment regimes, augmented inverse probability weighting, double robustness, local efficiency, sequential decision making, semiparametric estimation
- MeSH terms
- Algorithms, Clinical Trials as Topic, Decision Making, Longitudinal Studies, Probability, Research Design, Models, Statistical
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
