ArticleJournal of biopharmaceutical statistics2018
On Enrichment Strategies for Biomarker Stratified Clinical Trials.
Article in Journal of biopharmaceutical statistics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- A modular framework for antibody-drug conjugate biomarkers.Nature reviews. Clinical oncology · 2026Review
- Biomarker-guided adaptive enrichment design with threshold detection for clinical trials with time-to-event outcome.Journal of biopharmaceutical statistics · 2025Article
- Nanoparticle Therapeutics in Clinical Perspective: Classification, Marketed Products, and Regulatory Landscape.Small (Weinheim an der Bergstrasse, Germany) · 2025Review
- A roadmap for therapeutic discovery in pulmonary hypertension associated with left heart failure. A scientific statement of the Heart Failure Association (HFA) of the ESC and the ESC Working Group on Pulmonary Circulation & Right Ventricular Function.European journal of heart failure · 2024Article
- Interpreting Randomized Controlled Trials.Cancers · 2023Review
- Simulation study for evaluating an adaptive-randomisation Bayesian hybrid trial design with enrichment.Contemporary clinical trials communications · 2023Article
- Identification of early biomarkers of transcriptomics in alveolar macrophage for the prognosis of intubated ARDS patients.BMC pulmonary medicine · 2022Article
- Interpatient Heterogeneity in Drug Response and Protein Biomarker Expression of Recurrent Ovarian Cancer.Cancers · 2022Article
- Preoperative and Postoperative Systemic Therapy for Operable Non-Small-Cell Lung Cancer.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2022Review
- Statistical Considerations for Subgroup Analyses.Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer · 2021Article
- International harmonization in performing and reporting minimal residual disease assessment in multiple myeloma trials.Leukemia · 2021Review
- A Signature Enrichment Design with Bayesian Adaptive Randomization.Journal of applied statistics · 2021Article
- Design and analysis of biomarker-integrated clinical trials with adaptive threshold detection and flexible patient enrichment.Journal of biopharmaceutical statistics · 2020Article
- Auxiliary variable-enriched biomarker-stratified design.Statistics in medicine · 2018Article
- Cohort identification of axial spondyloarthritis in a large healthcare dataset: current and future methods.BMC musculoskeletal disorders · 2018Article
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Authors and funding
4 authors.
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Abstract
In the era of precision medicine, drugs are increasingly developed to target subgroups of patients with certain biomarkers. In large all-comer trials using a biomarker stratified design, the cost of treating and following patients for clinical outcomes may be prohibitive. With a fixed number of randomized patients, the efficiency of testing certain treatments parameters, including the treatment effect among biomarker-positive patients and the interaction between treatment and biomarker, can be improved by increasing the proportion of biomarker positives on study, especially when the prevalence rate of biomarker positives is low in the underlying patient population. When the cost of assessing the true biomarker is prohibitive, one can further improve the study efficiency by oversampling biomarker positives with a cheaper auxiliary variable or a surrogate biomarker that correlates with the true biomarker. To improve efficiency and reduce cost, we can adopt an enrichment strategy for both scenarios by concentrating on testing and treating patient subgroups that contain more information about specific treatment parameters of primary interest to the investigators. In the first scenario, an enriched biomarker stratified design enriches the cohort of randomized patients by directly oversampling the relevant patients with the true biomarker, while in the second scenario, an auxiliary-variable-enriched biomarker stratified design enriches the randomized cohort based on an inexpensive auxiliary variable, thereby avoiding testing the true biomarker on all screened patients and reducing treatment waiting time. For both designs, we discuss how to choose the optimal enrichment proportion when testing a single hypothesis or two hypotheses simultaneously. At a requisite power, we compare the two new designs with the BSD design in terms of the number of randomized patients and the cost of trial under scenarios mimicking real biomarker stratified trials. The new designs are illustrated with hypothetical examples for designing biomarker-driven cancer trials.
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