The DOI matches its Crossref record; 112 of the 112 references it cites were found in a registry or an index.
Dynamic Treatment Regimes
Open accessAbstract
A dynamic treatment regime consists of a sequence of decision rules, one per stage of intervention, that dictate how to individualize treatments to patients, based on evolving treatment and covariate history. These regimes are particularly useful for managing chronic disorders and fit well into the larger paradigm of personalized medicine. They provide one way to operationalize a clinical decision support system. Statistics plays a key role in the construction of evidence-based dynamic treatment regimes—informing the best study design as well as efficient estimation and valid inference. Owing to the many novel methodological challenges this area offers, it has been growing in popularity among statisticians in recent years. In this article, we review the key developments in this exciting field of research. In particular, we discuss the sequential multiple assignment randomized trial designs, estimation techniques like Q-learning and marginal structural models, and several inference techniques designed to address the associated nonstandard asymptotics. We reference software whenever available. We also outline some important future directions.
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Cites
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Personalized medicine: motivation, challenges, and progress
Goetz, L. H., & Schork, N. J. (2018) · Fertility and Sterility · doi:10.1016/j.fertnstert.2018.05.006 · Cited by 976
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Optimal medication dosing from suboptimal clinical examples: a deep reinforcement learning approach
Nemati, S., Ghassemi, M. M., & Clifford, G. D. (2016) · 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7591355 · Cited by 195
Treatment burden and treatment fatigue as barriers to health.
Heckman, B. W., Mathew, A. R., & Carpenter, M. J. (2015) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2015.03.004 · Cited by 179
Metadata
- Authors
- Chakraborty, B., & Murphy, S. A.
- Venue
- Annual Review of Statistics and Its Application
- Publisher
- Annual Reviews
- Published
- 2014-01-03
- Volume
- 1
- Issue
- 1
- Pages
- 447-464
- ISSN
- 2326-8298 (ISSN-L)
- 2326-831X
- OpenAlex
- W2134103864
- Cited by
- 286
- References
- 112
- FWCI
- 3.74
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
