ArticleBiometrics2020
An adaptive trial design to optimize dose-schedule regimes with delayed outcomes.
Article in Biometrics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- A Biomarker-Based Dose-Schedule Optimization Design for Immunotherapy Trials.Statistics in medicine · 2026Article
- Adaptive phase I-II clinical trial designs identifying optimal biological doses for targeted agents and immunotherapies.Clinical trials (London, England) · 2024Review
- A Bayesian adaptive design for dual-agent phase I-II oncology trials integrating efficacy data across stages.Biometrical journal. Biometrische Zeitschrift · 2023Article
- Concurrent dose-finding of a novel cancer drug with and without a second agent.Journal of clinical and translational science · 2023Article
- Optimizing dose-schedule regimens with bayesian adaptive designs: opportunities and challenges.Frontiers in pharmacology · 2023Review
- A Phase I-II Basket Trial Design to Optimize Dose-Schedule Regimes Based on Delayed Outcomes.Bayesian analysis · 2021Article
- Adaptive Enrichment Designs in Clinical Trials.Annual review of statistics and its application · 2021Article
- Prior Elicitation for Use in Clinical Trial Design and Analysis: A Literature Review.International journal of environmental research and public health · 2021Review
- A Personalized Dose-Finding Algorithm Based on Adaptive Gaussian Process Regression.Pharmaceutical statisticsArticle
- A model-assisted design for partially or completely ordered groups.Pharmaceutical statisticsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
This paper proposes a two-stage phase I-II clinical trial design to optimize dose-schedule regimes of an experimental agent within ordered disease subgroups in terms of the toxicity-efficacy trade-off. The design is motivated by settings where prior biological information indicates it is certain that efficacy will improve with ordinal subgroup level. We formulate a flexible Bayesian hierarchical model to account for associations among subgroups and regimes, and to characterize ordered subgroup effects. Sequentially adaptive decision-making is complicated by the problem, arising from the motivating application, that efficacy is scored on day 90 and toxicity is evaluated within 30 days from the start of therapy, while the patient accrual rate is fast relative to these outcome evaluation intervals. To deal with this in a practical manner, we take a likelihood-based approach that treats unobserved toxicity and efficacy outcomes as missing values, and use elicited utilities that quantify the efficacy-toxicity trade-off as a decision criterion. Adaptive randomization is used to assign patients to regimes while accounting for subgroups, with randomization probabilities depending on the posterior predictive distributions of utilities. A simulation study is presented to evaluate the design's performance under a variety of scenarios, and to assess its sensitivity to the amount of missing data, the prior, and model misspecification.
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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.