ArticleStatistics in medicine2019
Bivariate network meta-analysis for surrogate endpoint evaluation.
Article in Statistics in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 4 of them syntheses that pooled it.
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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
16 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Comparison of drug regimens for recurrent or metastatic cervical cancer: a systematic review and network meta-analysis.Frontiers in immunology · 2026Pooled it
- Evaluating amyloid-beta as a surrogate endpoint in trials of anti-amyloid-beta drugs in Alzheimer's disease: a Bayesian meta-analysis.Journal of comparative effectiveness research · 2026Pooled it
- Handling missing within-study correlations in the evaluation of surrogate endpoints.Statistics in medicine · 2023Pooled it
- Correlation Between Early Time-to-Event Outcomes and Overall Survival in Patients With Locally Advanced Head and Neck Squamous Cell Carcinoma Receiving Definitive Chemoradiation Therapy: Systematic Review and Meta-Analysis.Frontiers in oncology · 2022Pooled it
- A New Frequentist Implementation of the Daniels and Hughes Bivariate Meta-Analysis Model for Surrogate Endpoint Evaluation.Biometrical journal. Biometrische Zeitschrift · 2025Article
- Reporting of surrogate endpoints in randomised controlled trial reports (CONSORT-Surrogate): extension checklist with explanation and elaboration.BMJ (Clinical research ed.) · 2024Article
- Reporting of surrogate endpoints in randomised controlled trial protocols (SPIRIT-Surrogate): extension checklist with explanation and elaboration.BMJ (Clinical research ed.) · 2024Article
- Comparing Bayesian hierarchical meta-regression methods and evaluating the influence of priors for evaluations of surrogate endpoints on heterogeneous collections of clinical trials.BMC medical research methodology · 2024Article
- Using Bayesian Evidence Synthesis Methods to Incorporate Real-World Evidence in Surrogate Endpoint Evaluation.Medical decision making : an international journal of the Society for Medical Decision Making · 2023Article
- Article
- Article
- Development of a framework and decision tool for the evaluation of health technologies based on surrogate endpoint evidence.Health economics · 2022Article
- Review
- HTA methodology and value frameworks for evaluation and policy making for cell and gene therapies.The European journal of health economics : HEPAC : health economics in prevention and care · 2020Article
- Bayesian hierarchical meta-analytic methods for modeling surrogate relationships that vary across treatment classes using aggregate data.Statistics in medicine · 2020Article
- Bivariate network meta-analysis for surrogate endpoint evaluation.Statistics in medicine · 2019Article
Corrections and comments
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7 authors.
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Abstract
Surrogate endpoints are very important in regulatory decision making in healthcare, in particular if they can be measured early compared to the long-term final clinical outcome and act as good predictors of clinical benefit. Bivariate meta-analysis methods can be used to evaluate surrogate endpoints and to predict the treatment effect on the final outcome from the treatment effect measured on a surrogate endpoint. However, candidate surrogate endpoints are often imperfect, and the level of association between the treatment effects on the surrogate and final outcomes may vary between treatments. This imposes a limitation on methods which do not differentiate between the treatments. We develop bivariate network meta-analysis (bvNMA) methods, which combine data on treatment effects on the surrogate and final outcomes, from trials investigating multiple treatment contrasts. The bvNMA methods estimate the effects on both outcomes for all treatment contrasts individually in a single analysis. At the same time, they allow us to model the trial-level surrogacy patterns within each treatment contrast and treatment-level surrogacy, thus enabling predictions of the treatment effect on the final outcome either for a new study in a new population or for a new treatment. Modelling assumptions about the between-studies heterogeneity and the network consistency, and their impact on predictions, are investigated using an illustrative example in advanced colorectal cancer and in a simulation study. When the strength of the surrogate relationships varies across treatment contrasts, bvNMA has the advantage of identifying treatment comparisons for which surrogacy holds, thus leading to better predictions.
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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.