ArticleStatistics in medicine2023
Measuring the performance of prediction models to personalize treatment choice.
Article in Statistics in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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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.
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Who cites it
19 citing papers in PubMed.
- Low-Dose Aspirin for Individualized Cancer Prevention in Older Adults: A Secondary Analysis of the ASPREE Randomized Clinical Trial.JAMA oncology · 2025Trial
- Nonparametric Assessment of the Calibration of Individualized Treatment Effects.Statistics in medicine · 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
- Venous Nanoflap Oscillations: Biomechanical Determinants and Hydrodynamic Consequences in the Deep Cerebral Venous System.International journal of molecular sciences · 2026Review
- Evaluating treatment benefit predictors using observational data: contending with identification and confounding bias.American journal of epidemiology · 2026Article
- A Nomogram Predictive Model for Drug-Resistant Tuberculosis Detection by GeneXpert MTB/RIF Assay in Paediatric Patients.Infection and drug resistance · 2026Article
- Review
- Measuring the Performance of Survival Models to Personalize Treatment Choices.Statistics in medicine · 2025Article
- Article
- Article
- Predicting Individual Treatment Effects: Challenges and Opportunities for Machine Learning and Artificial Intelligence.Kunstliche intelligenz · 2025Article
- Development and validation of PRE-FRA (PREdiction of FRAilty risk in community older adults) frailty prediction model.Frontiers in public health · 2025Article
- Risk prediction models for enteral nutrition feeding intolerance in critically ill patients: an overview of systematic reviews.Frontiers in nutrition · 2025Review
- Uncovering individualised treatment effects for educational trials.Scientific reports · 2024Article
- Validation of predicted individual treatment effects in out of sample respondents.Statistics in medicine · 2024Article
- Framework for personalized prediction of treatment response in relapsing-remitting multiple sclerosis: a replication study in independent data.BMC medical research methodology · 2024Article
- 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
- Measuring the performance of prediction models to personalize treatment choice.Statistics in medicine · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
When data are available from individual patients receiving either a treatment or a control intervention in a randomized trial, various statistical and machine learning methods can be used to develop models for predicting future outcomes under the two conditions, and thus to predict treatment effect at the patient level. These predictions can subsequently guide personalized treatment choices. Although several methods for validating prediction models are available, little attention has been given to measuring the performance of predictions of personalized treatment effect. In this article, we propose a range of measures that can be used to this end. We start by defining two dimensions of model accuracy for treatment effects, for a single outcome: discrimination for benefit and calibration for benefit. We then amalgamate these two dimensions into an additional concept, decision accuracy, which quantifies the model's ability to identify patients for whom the benefit from treatment exceeds a given threshold. Subsequently, we propose a series of performance measures related to these dimensions and discuss estimating procedures, focusing on randomized data. Our methods are applicable for continuous or binary outcomes, for any type of prediction model, as long as it uses baseline covariates to predict outcomes under treatment and control. We illustrate all methods using two simulated datasets and a real dataset from a trial in depression. We implement all methods in the R package predieval. Results suggest that the proposed measures can be useful in evaluating and comparing the performance of competing models in predicting individualized treatment effect.
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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.