ArticleStatistics in medicine2021
A two-stage prediction model for heterogeneous effects of treatments.
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 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 28 citations in OpenAlex.
- Relapse prediction and individualized treatment-effect modeling in relapsing multiple sclerosis: a systematic review.Frontiers in public health · 2026Pooled it
- Estimating Patient-Specific Relative Benefit of Adding Biologics to Conventional Rheumatoid Arthritis Treatment: An Individual Participant Data Meta-Analysis.JAMA network open · 2023Pooled it
- Heterogeneous treatment effect of immune checkpoint inhibitors by pretreatment prognosis in randomized controlled trials.JNCI cancer spectrum · 2026Article
- Applicability of academic real-world data research in the case studies of the HTx project to practical health technology assessment work.Frontiers in pharmacology · 2026Article
- Predictive Modeling of Heterogeneous Treatment Effects in RCTs: A Scoping Review.JAMA network open · 2025Article
- Potential clinical impact of predictive modeling of heterogeneous treatment effects: scoping review of the impact of the PATH Statement.medRxiv : the preprint server for health sciences · 2025Article
- Prognostic models for predicting clinical disease progression, worsening and activity in people with multiple sclerosis.The Cochrane database of systematic reviews · 2023Review
- Assessing heterogeneity of treatment effect in multiple sclerosis trials.Multiple sclerosis (Houndmills, Basingstoke, England) · 2023Review
- Decision Curve Analysis for Personalized Treatment Choice between Multiple Options.Medical decision making : an international journal of the Society for Medical Decision Making · 2023Article
- Evidence-based Shared-Decision-Making Assistant (SDM-assistant) for choosing antipsychotics: protocol of a cluster-randomized trial in hospitalized patients with schizophrenia.BMC psychiatry · 2022Article
- Real World Data in Health Technology Assessment of Complex Health Technologies.Frontiers in pharmacology · 2022Article
- Development, validation and clinical usefulness of a prognostic model for relapse in relapsing-remitting multiple sclerosis.Diagnostic and prognostic research · 2021Article
- A two-stage prediction model for heterogeneous effects of treatments.Statistics in medicine · 2021Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Treatment effects vary across different patients, and estimation of this variability is essential for clinical decision-making. We aimed to develop a model estimating the benefit of alternative treatment options for individual patients, extending a risk modeling approach in a network meta-analysis framework. We propose a two-stage prediction model for heterogeneous treatment effects by combining prognosis research and network meta-analysis methods where individual patient data are available. In the first stage, a prognostic model to predict the baseline risk of the outcome. In the second stage, we use the baseline risk score from the first stage as a single prognostic factor and effect modifier in a network meta-regression model. We apply the approach to a network meta-analysis of three randomized clinical trials comparing the relapses in Natalizumab, Glatiramer Acetate, and Dimethyl Fumarate, including 3590 patients diagnosed with relapsing-remitting multiple sclerosis. We find that the baseline risk score modifies the relative and absolute treatment effects. Several patient characteristics, such as age and disability status, impact the baseline risk of relapse, which in turn moderates the benefit expected for each of the treatments. For high-risk patients, the treatment that minimizes the risk of relapse in 2 years is Natalizumab, whereas Dimethyl Fumarate might be a better option for low-risk patients. Our approach can be easily extended to all outcomes of interest and has the potential to inform a personalized treatment approach.
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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.