Trial reportThe American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry2018
A Machine Learning Approach to Identifying Placebo Responders in Late-Life Depression Trials.
Trial report in The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed, 41 citations in OpenAlex.
- A precision medicine tool to understand who responds best to hearing aids in late-life depression.International journal of geriatric psychiatry · 2022Trial
- Who benefits most from expectancy effects? A combined neuroimaging and antidepressant trial in depressed older adults.Translational psychiatry · 2021Trial
- The Construction and Validation of a new Predictive Model for Overall Survival of Clear Cell Renal Cell Carcinoma Patients with Bone Metastasis Based on Machine Learning Algorithm.Technology in cancer research & treatmentTrial
- Moderators of Internet-Delivered Cognitive-Behavioral Therapy for Adolescents With Chronic Pain: Who Benefits From Treatment at Long-Term Follow-Up?The journal of painTrial
- Artificial Intelligence and Predictive Modeling in the Management and Treatment of Episodic Migraine.Current pain and headache reports · 2025Review
- Machine learning meta-analysis identifies individual characteristics moderating cognitive intervention efficacy for anxiety and depression symptoms.NPJ digital medicine · 2025Article
- Comparison of Different Machine Learning Methodologies for Predicting the Non-Specific Treatment Response in Placebo Controlled Major Depressive Disorder Clinical Trials.Clinical and translational science · 2025Article
- Possible Strategies to Mitigate Placebo or Vehicle Response in Dry Eye Disease Trials: A Narrative Review.Ophthalmology and therapy · 2023Review
- Applying ensemble machine learning models to predict individual response to a digitally delivered worry postponement intervention.Journal of affective disorders · 2023Article
- Integrating exploration and prediction in computational psychotherapy science: proof of concept.Frontiers in psychiatry · 2023Article
- Development of a Machine Learning-Based Predictive Model for Lung Metastasis in Patients With Ewing Sarcoma.Frontiers in medicine · 2022Article
- Artificial Intelligence for Mental Health and Mental Illnesses: an Overview.Current psychiatry reports · 2019Review
- A Future Research Agenda for Digital Geriatric Mental Healthcare.The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry · 2019Article
- Can Machine Learning Approaches Lead Toward Personalized Cognitive Training?Frontiers in behavioral neuroscience · 2019Article
- Promise and Challenges of Using Combined Moderator Methods to Personalize Mental Health Treatment.The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry · 2018Article
- Using Artificial Intelligence-based Methods to Address the Placebo Response in Clinical Trials.Innovations in clinical neuroscienceReview
- Current and Emerging Technologies to Address the Placebo Response Challenge in CNS Clinical Trials: Promise, Pitfalls, and Pathways Forward.Innovations in clinical neuroscienceReview
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
objectiveDespite efforts to identify characteristics associated with medication-placebo differences in antidepressant trials, few consistent findings have emerged to guide participant selection in drug development settings and differential therapeutics in clinical practice. Limitations in the methodologies used, particularly searching for a single moderator while treating all other variables as noise, may partially explain the failure to generate consistent results. The present study tested whether interactions between pretreatment patient characteristics, rather than a single-variable solution, may better predict who is most likely to benefit from placebo versus medication.
methodsData were analyzed from 174 patients aged 75 years and older with unipolar depression who were randomly assigned to citalopram or placebo. Model-based recursive partitioning analysis was conducted to identify the most robust significant moderators of placebo versus citalopram response.
resultsThe greatest signal detection between medication and placebo in favor of medication was among patients with fewer years of education (≤12) who suffered from a longer duration of depression since their first episode (>3.47 years) (B = 2.53, t(32) = 3.01, p = 0.004). Compared with medication, placebo had the greatest response for those who were more educated (>12 years), to the point where placebo almost outperformed medication (B = -0.57, t(96) = -1.90, p = 0.06).
conclusionMachine learning approaches capable of evaluating the contributions of multiple predictor variables may be a promising methodology for identifying placebo versus medication responders. Duration of depression and education should be considered in the efforts to modulate placebo magnitude in drug development settings and in clinical practice.
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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.