ArticleCPT: pharmacometrics & systems pharmacology2021
Heterogeneous treatment effect analysis based on machine-learning methodology.
Article in CPT: pharmacometrics & systems pharmacology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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The trial behind it
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic Review on Artificial Intelligence in Rheumatology Practice: From Implementation Concerns to Imaging and Clinical Application.International journal of rheumatic diseases · 2026Pooled it
- 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
- Beyond Recanalization: Neurovascular Unit Protection and Precision Adjunctive Therapy for Ischemic Stroke in the Reperfusion Era.Biomolecules · 2026Review
- Heterogeneity of Clazosentan Benefit after Aneurysmal Subarachnoid Hemorrhage Demonstrated in an Externally Validated Causal Policy Tree.Neurocritical care · 2026Article
- Causal effect of video gaming on mental well-being in Japan 2020-2022.Nature human behaviour · 2024Article
- Estimating the Heterogeneous Causal Effects of Parent-Child Relationships among Chinese Children with Oppositional Defiant Symptoms: A Machine Learning Approach.Behavioral sciences (Basel, Switzerland) · 2024Article
- What is an exposure-response curve?Global epidemiology · 2023Article
- Effects of Psychological Interventions on Performance Anxiety in Performing Artists and Athletes: A Systematic Review with Meta-Analysis.Behavioral sciences (Basel, Switzerland) · 2023Review
- AI and machine learning in resuscitation: Ongoing research, new concepts, and key challenges.Resuscitation plus · 2023Review
- Comparison of causal forest and regression-based approaches to evaluate treatment effect heterogeneity: an application for type 2 diabetes precision medicine.BMC medical informatics and decision making · 2023Article
- Machine Learning for Exposure-Response Analysis: Methodological Considerations and Confirmation of Their Importance via Computational Experimentations.Pharmaceutics · 2023Article
- Heterogeneous treatment effects of Cerebrolysin as an early add-on to reperfusion therapy:Frontiers in pharmacology · 2023Article
- Predicting non-improvement of symptoms in daily mental healthcare practice using routinely collected patient-level data: a machine learning approach.Frontiers in psychiatry · 2023Article
- Heterogeneous treatment effect analysis based on machine-learning methodology.CPT: pharmacometrics & systems pharmacology · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Heterogeneous treatment effect (HTE) analysis focuses on examining varying treatment effects for individuals or subgroups in a population. For example, an HTE-informed understanding can critically guide physicians to individualize the medical treatment for a certain disease. However, HTE analysis has not been widely recognized and used, even given the explosive increase of data availability attributed to the arrival of the Big Data era. Part of the reason behind its underuse is that data are often of high dimension and high complexity, which pose significant challenges for applying conventional HTE analysis methods. To meet these challenges, a newly developed causal forest HTE method has been derived from the random forest machine-learning algorithm. We conducted a systematic performance evaluation for the causal forest method against the conventional two-step method by simulating scenarios with different levels of complexity for the analysis. Our results show that causal forest outperforms the conventional HTE method in assessing treatment effect, especially when data are complex (e.g., nonlinear) and high dimensional, suggesting that causal forest is a promising tool for real-world applications of HTE analysis.
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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.