ArticleStatistical methods in medical research2019
Optimized adaptive enrichment designs.
Article in Statistical methods in medical research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Confidence intervals and point estimates for treatment effects in adaptive enrichment designs.Statistical methods in medical research · 2026Article
- Two-stage Adaptive Enrichment Designs with Survival Outcomes and Adjustment for Misclassification in Predictive Biomarkers.Statistics in biopharmaceutical research · 2025Article
- Latest Developments in "Adaptive Enrichment" Clinical Trial Designs in Oncology.Therapeutic innovation & regulatory science · 2024Review
- Adaptive enrichment trial designs using joint modelling of longitudinal and time-to-event data.Statistical methods in medical research · 2024Article
- Use of Seamless Study Designs in Oncology Clinical Development- A Survey Conducted by IDSWG Oncology Sub-team.Therapeutic innovation & regulatory science · 2024Article
- Optimal futility stopping boundaries for binary endpoints.BMC medical research methodology · 2024Article
- Efficient testing of the biomarker positive and negative subgroups in a biomarker-stratified trial.Biometrics · 2024Article
- Inference on subgroups identified based on a heterogeneous treatment effect in a post hoc analysis of a clinical trial.Clinical trials (London, England) · 2023Article
- Point estimation for adaptive trial designs II: Practical considerations and guidance.Statistics in medicine · 2023Review
- Simulation study for evaluating an adaptive-randomisation Bayesian hybrid trial design with enrichment.Contemporary clinical trials communications · 2023Article
- Nonmyopic and pseudo-nonmyopic approaches to optimal sequential design in the presence of covariates.Journal of statistical computation and simulation · 2023Article
- Innovations in Clinical Development in Rare Diseases of Children and Adults: Small Populations and/or Small Patients.Paediatric drugs · 2022Review
- A Bayesian decision-theoretic design for simultaneous biomarker-based subgroup selection and efficacy evaluation.Statistics in biopharmaceutical research · 2022Article
- Article
- Collaborative Platform Trials to Fight COVID-19: Methodological and Regulatory Considerations for a Better Societal Outcome.Clinical pharmacology and therapeutics · 2021Review
- Optimizing subgroup selection in two-stage adaptive enrichment and umbrella designs.Statistics in medicine · 2021Article
- Adaptive enrichment trials: What are the benefits?Statistics in medicine · 2021Article
- Adding flexibility to clinical trial designs: an example-based guide to the practical use of adaptive designs.BMC medicine · 2020Article
- Optimality criteria for futility stopping boundaries for group sequential designs with a continuous endpoint.BMC medical research methodology · 2020Article
- Efficient Adaptive Designs for Clinical Trials of Interventions for COVID-19.Statistics in biopharmaceutical research · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Based on a Bayesian decision theoretic approach, we optimize frequentist single- and adaptive two-stage trial designs for the development of targeted therapies, where in addition to an overall population, a pre-defined subgroup is investigated. In such settings, the losses and gains of decisions can be quantified by utility functions that account for the preferences of different stakeholders. In particular, we optimize expected utilities from the perspectives both of a commercial sponsor, maximizing the net present value, and also of the society, maximizing cost-adjusted expected health benefits of a new treatment for a specific population. We consider single-stage and adaptive two-stage designs with partial enrichment, where the proportion of patients recruited from the subgroup is a design parameter. For the adaptive designs, we use a dynamic programming approach to derive optimal adaptation rules. The proposed designs are compared to trials which are non-enriched (i.e. the proportion of patients in the subgroup corresponds to the prevalence in the underlying population). We show that partial enrichment designs can substantially improve the expected utilities. Furthermore, adaptive partial enrichment designs are more robust than single-stage designs and retain high expected utilities even if the expected utilities are evaluated under a different prior than the one used in the optimization. In addition, we find that trials optimized for the sponsor utility function have smaller sample sizes compared to trials optimized under the societal view and may include the overall population (with patients from the complement of the subgroup) even if there is substantial evidence that the therapy is only effective in the subgroup.
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