Asymptotic Inference for Multi-Stage Stationary Treatment Policy with Variable Selection.

Daiqi Gao, Yufeng Liu, Donglin Zeng

Abstract read

In one paragraph

Article in Journal of machine learning research : JMLR, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

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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.

2 · The registry

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Daiqi GaoDepartment of Statistics, Harvard University, Cambridge, MA 02138, USA.

Yufeng LiuDepartment of Statistics and Operations Research, Department of Genetics, Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.

Donglin ZengDepartment of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.

Funding

Heart Steps: Adaptive mHealth intervention for physical-activity maintenanceR01HL125440 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Predrag Klasnja · 2015 to 2026

$3.8M

Statistical and Machine Learning Methods to Improve Dynamic Treatment Regimens Estimation Using Real World Data.R01GM124104 · NIGMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Yuanjia Wang, Donglin Zeng · 2018 to 2026

$3.1M

NHLBI NIH HHS R01 HL125440NIGMS NIH HHS R01 GM124104

6 · The paper itself

Abstract

Dynamic treatment regimes or policies are a sequence of decision functions over multiple stages that are tailored to individual features. One important class of treatment policies in practice, namely multi-stage stationary treatment policies, prescribes treatment assignment probabilities using the same decision function across stages, where the decision is based on the same set of features consisting of time-evolving variables (e.g., routinely collected disease biomarkers). Although there has been extensive literature on constructing valid inference for the value function associated with dynamic treatment policies, little work has focused on the policies themselves, especially in the presence of high-dimensional features. We aim to fill the gap in this work. Specifically, we first obtain the multi-stage stationary treatment policy by minimizing the negative augmented inverse probability weighted estimator of the value function to increase asymptotic efficiency. An

Indexed as

Augmented inverse probability weighted estimatordynamic treatment regimehigh-dimensional inferencepolicy parametersparse estimation

Identifiers

PMID41835788

PMCPMC12987690

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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.