ArticleStatistica Sinica2014
Estimation of treatment policies based on functional predictors.
Article in Statistica Sinica, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed.
- A sparse additive model for treatment effect-modifier selection.Biostatistics (Oxford, England) · 2022Trial
- Nonparametric Biomarker Based Treatment Selection With Reproducibility Data.Statistics in medicine · 2024Article
- Article
- A SEQUENTIAL SIGNIFICANCE TEST FOR TREATMENT BY COVARIATE INTERACTIONS.Statistica Sinica · 2021Article
- Threshold-based subgroup testing in logistic regression models in two-phase sampling designs.Journal of the Royal Statistical Society. Series C, Applied statistics · 2021Article
- Evaluating the Effectiveness of Personalized Medicine With Software.Frontiers in big data · 2021Article
- Identification of the optimal treatment regimen in the presence of missing covariates.Statistics in medicine · 2020Article
- Robust regression for optimal individualized treatment rules.Statistics in medicine · 2019Article
- Constructing treatment decision rules based on scalar and functional predictors when moderators of treatment effect are unknown.Journal of the Royal Statistical Society. Series C, Applied statistics · 2018Article
- Targeted learning ensembles for optimal individualized treatment rules with time-to-event outcomes.Biometrika · 2018Article
- A general statistical framework for subgroup identification and comparative treatment scoring.Biometrics · 2017Article
- Methods for scalar-on-function regression.International statistical review = Revue internationale de statistique · 2017Article
- Generated effect modifiers (GEM's) in randomized clinical trials.Biostatistics (Oxford, England) · 2017Article
- Interactive Q-learning for Quantiles.Journal of the American Statistical Association · 2017Article
- Functional feature construction for individualized treatment regimes.Journal of the American Statistical Association · 2017Article
- Flexible functional regression methods for estimating individualized treatment regimes.Stat (International Statistical Institute) · 2016Article
- Article
- Identifying optimal biomarker combinations for treatment selection through randomized controlled trials.Clinical trials (London, England) · 2015Article
- Identifying optimal biomarker combinations for treatment selection via a robust kernel method.Biometrics · 2014Article
- An approach to evaluating and comparing biomarkers for patient treatment selection.The international journal of biostatistics · 2014Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Biosignatures such as brain scans, mass spectrometry, or gene expression profiles might one day be used to guide treatment selection and improve outcomes. This article develops a way of estimating optimal treatment policies based on data from randomized clinical trials by interpreting patient biosignatures as functional predictors. A flexible functional regression model is used to represent the treatment effect and construct the estimated policy. The effectiveness of the estimated policy is assessed by furnishing prediction intervals for the mean outcome when all patients follow the policy. The validity of these prediction intervals is established under mild regularity conditions on the functional regression model. The performance of the proposed approach is evaluated in numerical studies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.