ArticleMedical decision making : an international journal of the Society for Medical Decision Making2022
A Machine-Learning Approach for Estimating Subgroup- and Individual-Level Treatment Effects: An Illustration Using the 65 Trial.
Article in Medical decision making : an international journal of the Society for Medical Decision Making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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The trial behind it
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Who cites it
14 citing papers in PubMed.
- Causal Forests in Practice: Lessons on Detecting Heterogeneous Treatment Effects in a Randomized Controlled Trial of a Healthy Food Subsidy Program in Canada.The Journal of nutrition · 2026Trial
- Advancing Drug Discovery with AI: Machine and Deep Learning Strategies for Target Identification and Precision Nanomedicine.International journal of nanomedicine · 2026Review
- Toward precision stroke rehabilitation: an integrated causal machine learning and clinician feedback approach.JAMIA open · 2025Article
- Machine Learning in Health Economic Evaluations: Protocol for a Scoping Review.JMIR research protocols · 2025Article
- Predictive Modeling of Heterogeneous Treatment Effects in RCTs: A Scoping Review.JAMA network open · 2025Article
- Generalized framework for identifying meaningful heterogenous treatment effects in observational studies: A parametric data-adaptive G-computation approach.Statistical methods in medical research · 2025Article
- Mean arterial pressure targets in intensive care unit patients receiving noradrenaline: An international survey.Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine · 2025Article
- Application of causal forests to randomised controlled trial data to identify heterogeneous treatment effects: a case study.BMC medical research methodology · 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
- Past, present, and future of Phase 3 vaccine trial design: rethinking statistics for the 21st century.Clinical and experimental immunology · 2025Review
- Machine Learning Methods to Estimate Individualized Treatment Effects for Use in Health Technology Assessment.Medical decision making : an international journal of the Society for Medical Decision Making · 2024Review
- Toward Precision in Critical Care Research: Methods for Observational and Interventional Studies.Critical care medicine · 2024Review
- Assessing the properties of patient-specific treatment effect estimates from causal forest algorithms under essential heterogeneity.BMC medical research methodology · 2024Article
- A machine-learning approach to estimating public intentions to become a living kidney donor in England: Evidence from repeated cross-sectional survey data.Frontiers in public health · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
highlightsThis article examines a causal machine-learning approach, causal forests (CF), for exploring the heterogeneity of treatment effects, without prespecifying a specific functional form.The CF approach is considered in the reanalysis of the 65 Trial and was found to provide similar estimates of subgroup effects to using a fixed parametric model.The CF approach also provides estimates of individual-level treatment effects that suggest that for most patients in the 65 Trial, the intervention is expected to reduce 90-d mortality but with wide levels of statistical uncertainty.The study illustrates how individual-level treatment effect estimates can be analyzed to generate hypotheses for further research about those patients who are likely to benefit most from an intervention.
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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.