ArticleBehaviour research and therapy2019
Machine learning methods for developing precision treatment rules with observational data.
Article in Behaviour research and therapy, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed.
- Prescriptive Predictors of Mindfulness Ecological Momentary Intervention for Social Anxiety Disorder: Machine Learning Analysis of Randomized Controlled Trial Data.JMIR mental health · 2025Trial
- Initial evaluation of a personalized advantage index to determine which individuals may benefit from mindfulness-based cognitive therapy for suicide prevention.Behaviour research and therapy · 2024Trial
- Innovations to improve outcomes and uptake of psychotherapies for mental disorders: a state-of-the-art review.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2026Article
- Individualized prescriptive inference in ischaemic stroke.Nature communications · 2025Article
- Article
- Post-discharge suicide prediction among US veterans using natural language processing-enriched social and behavioral determinants of health.Npj mental health research · 2025Article
- A survey on causal inference for recommendation.Innovation (Cambridge (Mass.)) · 2024Review
- The use of CAMS and DBT to effectively treat patients who are suicidal.Frontiers in psychiatry · 2024Review
- Predicting heterogeneous treatment effects of an Internet-based depression intervention for patients with chronic back pain: Secondary analysis of two randomized controlled trials.Internet interventions · 2023Article
- Evaluation of randomized controlled trials: a primer and tutorial for mental health researchers.Trials · 2023Article
- COBMC psychiatry · 2022Article
- Article
- Retiring, Rethinking, and Reconstructing the Norm of Once-Weekly Psychotherapy.Administration and policy in mental health · 2021Article
- Gut microbiome, big data and machine learning to promote precision medicine for cancer.Nature reviews. Gastroenterology & hepatology · 2020Review
- Perceived helpfulness of treatment for posttraumatic stress disorder: Findings from the World Mental Health Surveys.Depression and anxiety · 2020Article
- Supervised Machine Learning: A Brief Primer.Behavior therapy · 2020Review
- Improving Mental Health Services: A 50-Year Journey from Randomized Experiments to Artificial Intelligence and Precision Mental Health.Administration and policy in mental health · 2020Article
- Personalized Psychiatry and Depression: The Role of Sociodemographic and Clinical Variables.Psychiatry investigation · 2020Article
- Development and Validation of a Machine Learning Individualized Treatment Rule in First-Episode Schizophrenia.JAMA network open · 2020Article
- Why Are Suicide Rates Increasing in the United States? Towards a Multilevel Reimagination of Suicide Prevention.Current topics in behavioral neurosciences · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Clinical trials have identified a variety of predictor variables for use in precision treatment protocols, ranging from clinical biomarkers and symptom profiles to self-report measures of various sorts. Although such variables are informative collectively, none has proven sufficiently powerful to guide optimal treatment selection individually. This has prompted growing interest in the development of composite precision treatment rules (PTRs) that are constructed by combining information across a range of predictors. But this work has been hampered by the generally small samples in randomized clinical trials and the use of suboptimal analysis methods to analyze the resulting data. In this paper, we propose to address the sample size problem by: working with large observational electronic medical record databases rather than controlled clinical trials to develop preliminary PTRs; validating these preliminary PTRs in subsequent pragmatic trials; and using ensemble machine learning methods rather than individual algorithms to carry out statistical analyses to develop the PTRs. The major challenges in this proposed approach are that treatment are not randomly assigned in observational databases and that these databases often lack measures of key prescriptive predictors and mental disorder treatment outcomes. We proposed a tiered case-cohort design approach that uses innovative methods for measuring and balancing baseline covariates and estimating PTRs to address these challenges.
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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.