Estimation and extrapolation of optimal treatment and testing strategies
Abstract
We review recent developments in the estimation of an optimal treatment strategy or regime from longitudinal data collected in an observational study. We also propose novel methods for using the data obtained from an observational database in one health‐care system to determine the optimal treatment regime for biologically similar subjects in a second health‐care system when, for cultural, logistical, or financial reasons, the two health‐care systems differ (and will continue to differ) in the frequency of, and reasons for, both laboratory tests and physician visits. Finally, we propose a novel method for estimating the optimal timing of expensive and/or painful diagnostic or prognostic tests. Diagnostic or prognostic tests are only useful in so far as they help a physician to determine the optimal dosing strategy, by providing information on both the current health state and the prognosis of a patient because, in contrast to drug therapies, these tests have no direct causal effect on disease progression. Our new method explicitly incorporates this no direct effect restriction. Copyright © 2008 John Wiley & Sons, Ltd.
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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 [68]
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Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models
Scharfstein, D. O., Rotnitzky, A. G., & Robins, J. M. (1999) · Journal of the American Statistical Association · doi:10.1080/01621459.1999.10473862 · Cited by 1,021 · 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
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-citationStatistical Models in Epidemiology, the Environment, and Clinical Trials
Halloran, M. E., & Berry, D. A. (2000) · The IMA volumes in mathematics and its applications · doi:10.1007/978-1-4612-1284-3 · Cited by 547 · see citing works
Correcting for non-compliance in randomized trials using structural nested mean models
Robins, J. M. (1994) · Communication in Statistics- Theory and Methods · doi:10.1080/03610929408831393 · Cited by 534 · see citing works
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Self-citationComparison of Dynamic Treatment Regimes via Inverse Probability Weighting
Hernán, M. Á., Lanoy, É., Costagliola, D. G., & Robins, J. M. (2006) · Basic & Clinical Pharmacology & Toxicology · doi:10.1111/j.1742-7843.2006.pto_329.x · Cited by 281 · see citing works
Self-citationDemystifying Optimal Dynamic Treatment Regimes
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Predictors identified for losses to follow-up among HIV-seropositive patients
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Causal Effect Models for Intention to Treat and Realistic Individualized Treatment Rules
van der Laan, M. J. (2006) · Collection of Biostatistics Research Archive · Cited by 18 · see citing works
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Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available: Table 1.
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Self-citationDiagnosing and responding to violations in the positivity assumption
Petersen, M. L., Porter, K. E., Gruber, S., Wang, Y., & van der Laan, M. J. (2012) · Statistical Methods in Medical Research · doi:10.1177/0962280210386207 · Cited by 548 · see citing works
A "SMART" Design for Building Individualized Treatment Sequences
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Experimental design and primary data analysis methods for comparing adaptive interventions.
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Self-citationWang, 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
“…regime estimation (Murphy 2003; Robins 2004) and inverse probability weighted estimation of marginal structural models (Murphy, van der Laan, Robins, and CPPRG 2001; van der Laan 2006; van der Laan and Petersen 2007; Robins, Orellana, and Rotnitzky 2008; Orellana, Rotnitzky, and Robin 2010).”
Self-citation
- Authors
- Robins, J., Orellana, L., & Rotnitzky, A.
- Affiliations
- Harvard University (US)
- Universidad de Buenos Aires (AR)
- Corresponding author
- Robins, James
- Venue
- Statistics in Medicine
- Publisher
- Wiley
- Published
- 2008-08-28
- Volume
- 27
- Issue
- 23
- Pages
- 4678-4721
- ISSN
- 0277-6715 (ISSN-L)
- 1097-0258
- OpenAlex
- W2127008730
- Cited by
- 208
- References
- 15
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 4 · 2023: 13 · 2022: 14 · 2021: 10 · 2020: 15 · 2019: 11 · 2018: 18 · 2017: 14 · 2016: 16 · 2015: 12 · 2014: 23 · 2013: 10 · 2012: 16
- FWCI
- 8.82
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Statistics in Medicine (publishedVersion)
- PubMed (publishedVersion)
- Deakin Research Online (Deakin University) (submittedVersion)
- Funders
- U.S. Public Health Service (R01132475)
- 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
- optimal treatment regimes, longitudinal observational data, prognostic tests, causal inference, longitudinal analysis
- MeSH terms
- Data Interpretation, Statistical, Humans, Longitudinal Studies, Prognosis, Models, Statistical, HIV Infections, Bias, Treatment Outcome, Antiretroviral Therapy, Highly Active
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