ArticleElectronic journal of statistics2018
High-Dimensional Inference for Personalized Treatment Decision.
Article in Electronic journal of statistics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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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
13 citing papers in PubMed.
- A sparse additive model for treatment effect-modifier selection.Biostatistics (Oxford, England) · 2022Trial
- Asymptotic Inference for Multi-Stage Stationary Treatment Policy with Variable Selection.Journal of machine learning research : JMLR · 2025Article
- A Bayesian multivariate hierarchical model for developing a treatment benefit index using mixed types of outcomes.BMC medical research methodology · 2024Article
- Variable selection in high dimensions for discrete-outcome individualized treatment rules: Reducing severity of depression symptoms.Biostatistics (Oxford, England) · 2024Article
- Variable selection for individualised treatment rules with discrete outcomes.Journal of the Royal Statistical Society. Series C, Applied statistics · 2024Article
- Improving Individualized Treatment Decisions: A Bayesian Multivariate Hierarchical Model for Developing a Treatment Benefit Index using Mixed Types of Outcomes.medRxiv : the preprint server for health sciences · 2024Article
- Article
- Bayesian Index Models for Heterogeneous Treatment Effects on a Binary Outcome.Statistics in biosciences · 2023Article
- Estimation and inference on high-dimensional individualized treatment rule in observational data using split-and-pooled de-correlated score.Journal of machine learning research : JMLR · 2022Article
- A constrained single-index regression for estimating interactions between a treatment and covariates.Biometrics · 2021Article
- Ascertaining properties of weighting in the estimation of optimal treatment regimes under monotone missingness.Statistics in medicine · 2020Article
- A single-index model with multiple-links.Journal of statistical planning and inference · 2020Article
- Precision Medicine.Annual review of statistics and its application · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Recent development in statistical methodology for personalized treatment decision has utilized high-dimensional regression to take into account a large number of patients' covariates and described personalized treatment decision through interactions between treatment and covariates. While a subset of interaction terms can be obtained by existing variable selection methods to indicate relevant covariates for making treatment decision, there often lacks statistical interpretation of the results. This paper proposes an asymptotically unbiased estimator based on Lasso solution for the interaction coefficients. We derive the limiting distribution of the estimator when baseline function of the regression model is unknown and possibly misspecified. Confidence intervals and p-values are derived to infer the effects of the patients' covariates in making treatment decision. We confirm the accuracy of the proposed method and its robustness against misspecified function in simulation and apply the method to STAR*D study for major depression disorder.
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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.