ReviewJournal of biomedical informatics2023
Emulate randomized clinical trials using heterogeneous treatment effect estimation for personalized treatments: Methodology review and benchmark.
Review in Journal of biomedical informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed.
- Trial
- Trial emulation for validating the clinical efficacy of a foundational AI model in embryo selection.NPJ digital medicine · 2026Article
- Comparing Alternative Approaches to Care Management Prioritization: A Prospective Comparative Cohort Study of Acute Care Utilization and Equity Among Medicaid Beneficiaries.Health services research · 2026Article
- Evaluating the effectiveness and safety of Xiyanping injection for severe pneumonia: a target trial emulation protocol using real-world data.Frontiers in pharmacology · 2026Article
- From precision interventions to precision health.Nature communications · 2025Review
- Advancing efficacy prediction for electronic health records based emulated trials in repurposing heart failure therapies.NPJ digital medicine · 2025Article
- Deep learning for causal inference using low birth weight in midwife-led continuity care intervention in north Shoa zone, Ethiopia.Frontiers in artificial intelligence · 2025Article
- Causal effect of video gaming on mental well-being in Japan 2020-2022.Nature human behaviour · 2024Article
- Emerging Therapeutic Targets for Acute Coronary Syndromes: Novel Advancements and Future Directions.Biomedicines · 2024Review
- Factors Influencing Neuromuscular Blockade Reversal Choice in the United States Before and During the COVID-19 Pandemic: Retrospective Longitudinal Analysis.JMIR perioperative medicine · 2024Article
- An interpretable framework to identify responsive subgroups from clinical trials regarding treatment effects: Application to treatment of intracerebral hemorrhage.PLOS digital health · 2024Article
- Making causal inferences from transactional data: A narrative review of opportunities and challenges when implementing the target trial framework.The Journal of international medical research · 2024Review
- Inferring Personalized Treatment Effect of Antihypertensives on Alzheimer's Disease Using Deep Learning.Proceedings. IEEE International Conference on Healthcare Informatics · 2023Article
- Prescription Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) and Incidence of Depression Among Older Cancer Survivors With Osteoarthritis: A Machine Learning Analysis.Cancer informatics · 2023Article
- Heterogeneous treatment effects of Cerebrolysin as an early add-on to reperfusion therapy:Frontiers in pharmacology · 2023Article
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
Big data and (deep) machine learning have been ambitious tools in digital medicine, but these tools focus mainly on association. Intervention in medicine is about the causal effects. The average treatment effect has long been studied as a measure of causal effect, assuming that all populations have the same effect size. However, no "one-size-fits-all" treatment seems to work in some complex diseases. Treatment effects may vary by patient. Estimating heterogeneous treatment effects (HTE) may have a high impact on developing personalized treatment. Lots of advanced machine learning models for estimating HTE have emerged in recent years, but there has been limited translational research into the real-world healthcare domain. To fill the gap, we reviewed and compared eleven recent HTE estimation methodologies, including meta-learner, representation learning models, and tree-based models. We performed a comprehensive benchmark experiment based on nationwide healthcare claim data with application to Alzheimer's disease drug repurposing. We provided some challenges and opportunities in HTE estimation analysis in the healthcare domain to close the gap between innovative HTE models and deployment to real-world healthcare problems.
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.