ArticleStatistics in medicine2018
Identifying optimal dosage regimes under safety constraints: An application to long term opioid treatment of chronic pain.
Article in Statistics in medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Association between dynamic dose increases of buprenorphine for treatment of opioid use disorder and risk of relapse.Addiction (Abingdon, England) · 2022Trial
- A Bayesian multivariate hierarchical model for developing a treatment benefit index using mixed types of outcomes.BMC medical research methodology · 2024Article
- Fusing Individualized Treatment Rules Using Secondary Outcomes.Proceedings of machine learning research · 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
- Controlling Cumulative Adverse Risk in Learning Optimal Dynamic Treatment Regimens.Journal of the American Statistical Association · 2024Article
- Learning Optimal Dynamic Treatment Regimens Subject to Stagewise Risk Controls.Journal of machine learning research : JMLR · 2024Article
- Buprenorphine & methadone dosing strategies to reduce risk of relapse in the treatment of opioid use disorder.Drug and alcohol dependence · 2022Article
- A single-index model with a surface-link for optimizing individualized dose rules.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2022Article
- Estimating optimal dynamic treatment strategies under resource constraints using dynamic marginal structural models.Statistics in medicine · 2021Article
- Estimation and Optimization of Composite Outcomes.Journal of machine learning research : JMLR · 2021Article
- Bayesian Nonparametric Policy Search with Application to Periodontal Recall Intervals.Journal of the American Statistical Association · 2020Article
- Precision Medicine.Annual review of statistics and its application · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
There is growing interest and investment in precision medicine as a means to provide the best possible health care. A treatment regime formalizes precision medicine as a sequence of decision rules, one per clinical intervention period, that specify if, when and how current treatment should be adjusted in response to a patient's evolving health status. It is standard to define a regime as optimal if, when applied to a population of interest, it maximizes the mean of some desirable clinical outcome, such as efficacy. However, in many clinical settings, a high-quality treatment regime must balance multiple competing outcomes; eg, when a high dose is associated with substantial symptom reduction but a greater risk of an adverse event. We consider the problem of estimating the most efficacious treatment regime subject to constraints on the risk of adverse events. We combine nonparametric Q-learning with policy-search to estimate a high-quality yet parsimonious treatment regime. This estimator applies to both observational and randomized data, as well as settings with variable, outcome-dependent follow-up, mixed treatment types, and multiple time points. This work is motivated by and framed in the context of dosing for chronic pain; however, the proposed framework can be applied generally to estimate a treatment regime which maximizes the mean of one primary outcome subject to constraints on one or more secondary outcomes. We illustrate the proposed method using data pooled from 5 open-label flexible dosing clinical trials for chronic pain.
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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.