The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Performance guarantees for individualized treatment rules
Open accessAbstract
Because many illnesses show heterogeneous response to treatment, there is increasing interest in individualizing treatment to patients [11]. An individualized treatment rule is a decision rule that recommends treatment according to patient characteristics. We consider the use of clinical trial data in the construction of an individualized treatment rule leading to highest mean response. This is a difficult computational problem because the objective function is the expectation of a weighted indicator function that is non-concave in the parameters. Furthermore there are frequently many pretreatment variables that may or may not be useful in constructing an optimal individualized treatment rule yet cost and interpretability considerations imply that only a few variables should be used by the individualized treatment rule. To address these challenges we consider estimation based on l(1) penalized least squares. This approach is justified via a finite sample upper bound on the difference between the mean response due to the estimated individualized treatment rule and the mean response due to the optimal individualized treatment rule.
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Convexity, Classification, and Risk Bounds
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Optimal Dynamic Treatment Regimes
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Self-citationOptimal aggregation of classifiers in statistical learning
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A Robust Method for Estimating Optimal Treatment Regimes
Zhang, B., Tsiatis, A. A., Laber, E. B., & Davidian, M. (2012) · Biometrics · doi:10.1111/j.1541-0420.2012.01763.x · Cited by 422
Reinforcement learning for intelligent healthcare applications: A survey
Coronato, A., Naeem, M., De Pietro, G., & Paragliola, G. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101964 · Cited by 320
Tutorial in biostatistics: data‐driven subgroup identification and analysis in clinical trials
Lipkovich, I., Dmitrienko, A., & B., R. (2017) · Statistics in Medicine · doi:10.1002/sim.7064 · Cited by 291
Chakraborty, B., & Murphy, S. A. (2014) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-022513-115553 · Cited by 286
Self-citationWho Should Be Treated? Empirical Welfare Maximization Methods for Treatment Choice
Kitagawa, T., & Tetenov, A. (2018) · Econometrica · doi:10.3982/ecta13288 · Cited by 277
Metadata
- Authors
- Qian, M., & Murphy, S. A.
- Venue
- The Annals of Statistics
- Publisher
- Institute of Mathematical Statistics
- Published
- 2011-04-01
- Volume
- 39
- Issue
- 2
- Pages
- 1180-1210
- ISSN
- 0090-5364 (ISSN-L)
- OpenAlex
- W2164985671
- Cited by
- 444
- References
- 32
- FWCI
- 6.68
- Type
- journal article
- Language
- en
- Open access
- bronze
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
