Trial reportJournal of addiction medicine
Machine Learning-Driven Analysis of Individualized Treatment Effects Comparing Buprenorphine and Naltrexone in Opioid Use Disorder Relapse Prevention.
Trial report in Journal of addiction medicine. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Integrating Psychosocial Factors into Artificial Intelligence Models for Predicting Addiction Treatment Outcomes: A Systematic Review.European addiction research · 2026Pooled it
- Predictive Modeling of Heterogeneous Treatment Effects in RCTs: A Scoping Review.JAMA network open · 2025Article
- Potential clinical impact of predictive modeling of heterogeneous treatment effects: scoping review of the impact of the PATH Statement.medRxiv : the preprint server for health sciences · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
objectiveA trial comparing extended-release naltrexone and sublingual buprenorphine-naloxone demonstrated higher relapse rates in individuals randomized to extended-release naltrexone. The effectiveness of treatment might vary based on patient characteristics. We hypothesized that causal machine learning would identify individualized treatment effects for each medication.
methodsThis is a secondary analysis of a multicenter randomized trial that compared the effectiveness of extended-release naltrexone versus buprenorphine-naloxone for preventing relapse of opioid misuse. Three machine learning models were derived using all trial participants with 50% randomly selected for training (n = 285) and the remaining 50% for validation. Individualized treatment effect was measured by the Qini value and c-for-benefit, with the absence of relapse denoting treatment success. Patients were grouped into quartiles by predicted individualized treatment effect to examine differences in characteristics and the observed treatment effects.
resultsThe best-performing model had a Qini value of 4.45 (95% confidence interval, 1.02-7.83) and a c-for-benefit of 0.63 (95% confidence interval, 0.53-0.68). The quartile most likely to benefit from buprenorphine-naloxone had a 35% absolute benefit from this treatment, and at study entry, they had a high median opioid withdrawal score ( P < 0.001), used cocaine on more days over the prior 30 days than other quartiles ( P < 0.001), and had highest proportions with alcohol and cocaine use disorder ( P ≤ 0.02). Quartile 4 individuals were predicted to be most likely to benefit from extended-release naltrexone, with the greatest proportion having heroin drug preference ( P = 0.02) and all experiencing homelessness ( P < 0.001).
conclusionsCausal machine learning identified differing individualized treatment effects between medications based on characteristics associated with preventing relapse.
Indexed as
Identifiers
What OpenQuestion holds
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.