ArticleStatistics in medicine2020
Constructing dynamic treatment regimes with shared parameters for censored data.
Article in Statistics in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Censoring-adjusted tree-based policy learning for estimating dynamic treatment regimes with censored outcomes.Scientific reports · 2026Article
- Estimation of optimal treatment regimes with electronic medical record data using the residual life value estimator.Biostatistics (Oxford, England) · 2024Article
- Constructing time-invariant dynamic surveillance rules for optimal monitoring schedules.Biometrics · 2023Article
- Accountable survival contrast-learning for optimal dynamic treatment regimes.Scientific reports · 2023Article
- GENERALIZATION ERROR BOUNDS OF DYNAMIC TREATMENT REGIMES IN PENALIZED REGRESSION-BASED LEARNING.Annals of statistics · 2022Article
- A scoping review of studies using observational data to optimise dynamic treatment regimens.BMC medical research methodology · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Dynamic treatment regimes are sequential decision rules that adapt throughout disease progression according to a patient's evolving characteristics. In many clinical applications, it is desirable that the format of the decision rules remains consistent over time. Unlike the estimation of dynamic treatment regimes in regular settings, where decision rules are formed without shared parameters, the derivation of the shared decision rules requires estimating shared parameters indexing the decision rules across different decision points. Estimation of such rules becomes more complicated when the clinical outcome of interest is a survival time subject to censoring. To address these challenges, we propose two novel methods: censored shared-Q-learning and censored shared-O-learning. Both methods incorporate clinical preferences into a qualitative rule, where the parameters indexing the decision rules are shared across different decision points and estimated simultaneously. We use simulation studies to demonstrate the superior performance of the proposed methods. The methods are further applied to the Framingham Heart Study to derive treatment rules for cardiovascular disease.
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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.