The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
A Robust Method for Estimating Optimal Treatment Regimes
Open accessAbstract
Summary A treatment regime is a rule that assigns a treatment, among a set of possible treatments, to a patient as a function of his/her observed characteristics, hence “personalizing” treatment to the patient. The goal is to identify the optimal treatment regime that, if followed by the entire population of patients, would lead to the best outcome on average. Given data from a clinical trial or observational study, for a single treatment decision, the optimal regime can be found by assuming a regression model for the expected outcome conditional on treatment and covariates, where, for a given set of covariates, the optimal treatment is the one that yields the most favorable expected outcome. However, treatment assignment via such a regime is suspect if the regression model is incorrectly specified. Recognizing that, even if misspecified, such a regression model defines a class of regimes, we instead consider finding the optimal regime within such a class by finding the regime that optimizes an estimator of overall population mean outcome. To take into account possible confounding in an observational study and to increase precision, we use a doubly robust augmented inverse probability weighted estimator for this purpose. Simulations and application to data from a breast cancer clinical trial demonstrate the performance of the method.
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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
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Estimating heterogeneous treatment effects with right-censored data via causal survival forests
Cui, Y., Kosorok, M. R., Sverdrup, E., Wager, S., & Zhu, R. (2023) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1093/jrsssb/qkac001 · Cited by 117
General guidance on exploratory and confirmatory subgroup analysis in late-stage clinical trials
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Metadata
- Authors
- Zhang, B., Tsiatis, A. A., Laber, E. B., & Davidian, M.
- Venue
- Biometrics
- Publisher
- Oxford University Press (OUP)
- Published
- 2012-05-02
- Volume
- 68
- Issue
- 4
- Pages
- 1010-1018
- ISSN
- 0006-341X (ISSN-L)
- 1541-0420
- OpenAlex
- W2066153095
- Cited by
- 422
- References
- 22
- FWCI
- 21.57
- Type
- journal article
- Language
- en
- Open access
- green
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