ArticleStatistics in medicine2021
A tutorial on individualized treatment effect prediction from randomized trials with a binary endpoint.
Article in Statistics in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 1 of them a synthesis that pooled it.
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
41 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Clinical prediction models for mortality in patients with covid-19: external validation and individual participant data meta-analysis.BMJ (Clinical research ed.) · 2022Pooled it
- Predicting clinical response in psoriatic arthritis through integrative analysis of transcriptomics and proteomics.Arthritis research & therapy · 2026Trial
- Individualized Treatment Effects of a Digital Smoking Cessation Intervention Among Individuals Looking Online for Help: Secondary Analysis of a Randomized Controlled Trial.JMIR mHealth and uHealth · 2026Trial
- The development of a clinical prediction model for response to methotrexate, tofacitinib, and etanercept in patients with Psoriatic Arthritis.Arthritis research & therapy · 2025Trial
- Individualized transfusion decisions to minimize adverse cardiovascular outcomes in patients with acute myocardial infarction and anemia.American heart journal · 2025Trial
- Individualized treatment effects of a digital alcohol intervention and their associations with participant characteristics and engagement.Alcohol and alcoholism (Oxford, Oxfordshire) · 2024Trial
- The potential benefit of statin prescription based on prediction of treatment responsiveness in older individuals: an application to the PROSPER randomized controlled trial.European journal of preventive cardiology · 2024Trial
- Delayed versus primary closure to minimize risk of surgical-site infection for complicated appendicitis: A secondary analysis of a randomized trial using counterfactual prediction modeling.Infection control and hospital epidemiology · 2024Trial
- Conditional Treatment Effect Analysis of Two Infusion Rates for Fluid Challenges in Critically Ill Patients: A Secondary Analysis of Balanced Solution versus Saline in Intensive Care Study (BaSICS) Trial.Annals of the American Thoracic Society · 2023Trial
- Machine Learning-Driven Analysis of Individualized Treatment Effects Comparing Buprenorphine and Naltrexone in Opioid Use Disorder Relapse Prevention.Journal of addiction medicineTrial
- Machine Learning in Causal Effect Estimation and Causal Evaluation of ML-Enabled Interventions in Nursing-Related Research: A Scoping Review.Nursing & health sciences · 2026Article
- Machine learning methods for estimating personalized treatment effects-insights on validity from two large trials.American journal of epidemiology · 2026Article
- Prediction of Treatment Benefit With Internet-Based Cognitive Behavioral Therapy for Depression.JAMA network open · 2026Article
- Heterogeneity in the treatment effect of sodium-glucose cotransporter-2 inhibitor on chronic kidney disease risk reduction: a prevalent new user cohort study applying double/debiased machine learning.Diabetology & metabolic syndrome · 2026Article
- Variation in effectiveness of blood transfusion by hospital day: A multicenter retrospective study.Journal of hospital medicine · 2026Article
- Article
- Language-informed treatment recommendations in psychiatry: bridging the gap in individual predictability.Frontiers in psychiatry · 2026Article
- Article
- Contemporary reperfusion therapies in patients with intermediate- and high-risk pulmonary embolism.Thrombosis research · 2025Article
- Individualised treatment effect prediction: the benefit of initial biological treatment in early rheumatoid arthritis.EULAR rheumatology open · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Randomized trials typically estimate average relative treatment effects, but decisions on the benefit of a treatment are possibly better informed by more individualized predictions of the absolute treatment effect. In case of a binary outcome, these predictions of absolute individualized treatment effect require knowledge of the individual's risk without treatment and incorporation of a possibly differential treatment effect (ie, varying with patient characteristics). In this article, we lay out the causal structure of individualized treatment effect in terms of potential outcomes and describe the required assumptions that underlie a causal interpretation of its prediction. Subsequently, we describe regression models and model estimation techniques that can be used to move from average to more individualized treatment effect predictions. We focus mainly on logistic regression-based methods that are both well-known and naturally provide the required probabilistic estimates. We incorporate key components from both causal inference and prediction research to arrive at individualized treatment effect predictions. While the separate components are well known, their successful amalgamation is very much an ongoing field of research. We cut the problem down to its essentials in the setting of a randomized trial, discuss the importance of a clear definition of the estimand of interest, provide insight into the required assumptions, and give guidance with respect to modeling and estimation options. Simulated data illustrate the potential of different modeling options across scenarios that vary both average treatment effect and treatment effect heterogeneity. Two applied examples illustrate individualized treatment effect prediction in randomized trial data.
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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.