ArticleBiostatistics (Oxford, England)2017
Generated effect modifiers (GEM's) in randomized clinical trials.
Article in Biostatistics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- A Machine Learning Approach to Identifying Placebo Responders in Late-Life Depression Trials.The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry · 2018Trial
- Targeting mechanisms of change as a path to precision psychotherapy for community settings.Psychotherapy research : journal of the Society for Psychotherapy Research · 2026Article
- A Bayesian multivariate hierarchical model for developing a treatment benefit index using mixed types of outcomes.BMC medical research methodology · 2024Article
- Improving Individualized Treatment Decisions: A Bayesian Multivariate Hierarchical Model for Developing a Treatment Benefit Index using Mixed Types of Outcomes.medRxiv : the preprint server for health sciences · 2024Article
- A Single Index Model for Longitudinal Outcomes to Optimize Individual Treatment Decision Rules.Stat · 2022Article
- Development and Validation of a Treatment Benefit Index to Identify Hospitalized Patients With COVID-19 Who May Benefit From Convalescent Plasma.JAMA network open · 2022Article
- The concept of justifiable healthcare and how big data can help us to achieve it.BMC medical informatics and decision making · 2021Review
- Predictive approaches to heterogeneous treatment effects: a scoping review.BMC medical research methodology · 2020Article
- Predicting Depression Symptoms in Families at Risk for Depression: Interrelations of Posterior EEG Alpha and Religion/Spirituality.Journal of affective disorders · 2020Article
- A single-index model with multiple-links.Journal of statistical planning and inference · 2020Article
- The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement: Explanation and Elaboration.Annals of internal medicine · 2020Article
- Optimising treatment decision rules through generated effect modifiers: a precision medicine tutorial.BJPsych open · 2019Article
- Statistical Analysis Plan for Stage 1 EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care) Study.Contemporary clinical trials communications · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
In a randomized clinical trial (RCT), it is often of interest not only to estimate the effect of various treatments on the outcome, but also to determine whether any patient characteristic has a different relationship with the outcome, depending on treatment. In regression models for the outcome, if there is a non-zero interaction between treatment and a predictor, that predictor is called an "effect modifier". Identification of such effect modifiers is crucial as we move towards precision medicine, that is, optimizing individual treatment assignment based on patient measurements assessed when presenting for treatment. In most settings, there will be several baseline predictor variables that could potentially modify the treatment effects. This article proposes optimal methods of constructing a composite variable (defined as a linear combination of pre-treatment patient characteristics) in order to generate an effect modifier in an RCT setting. Several criteria are considered for generating effect modifiers and their performance is studied via simulations. An example from a RCT is provided for illustration.
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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.