ArticleBiostatistics (Oxford, England)2022
A benchmark for dose-finding studies with unknown ordering.
Article in Biostatistics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Applying the Partial Order Continual Reassessment Method to High-Dimensional Treatment Combinations.Statistics in medicine · 2026Article
- Joint time-to-event partial order continual reassessment method and Joint time-to-event Bayesian logistic regression model: Statistical designs for dual agent phase I/II dose finding studies with late-onset toxicity and activity outcomes.Statistical methods in medical research · 2026Article
- A comparison of model-free phase I dose escalation designs for dual-agent combination therapies.Statistical methods in medical research · 2024Article
- Practical implementation of the partial ordering continual reassessment method in a Phase I combination-schedule dose-finding trial.Statistics in medicine · 2022Article
- Flexible use of copula-type model for dose-finding in drug combination clinical trials.Biometrics · 2022Article
- Practical recommendations for implementing a Bayesian adaptive phase I design during a pandemic.BMC medical research methodology · 2022Article
- Operating characteristics are needed to properly evaluate the scientific validity of phase I protocols.Contemporary clinical trials · 2021Article
- Using an Interaction Parameter in Model-Based Phase I Trials for Combination Treatments? A Simulation Study.International journal of environmental research and public health · 2021Article
- Early Phase Dose-Finding Designs for CAR-T Cell Therapies.Pharmaceutical statisticsArticle
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3 authors.
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Abstract
An important tool to evaluate the performance of a dose-finding design is the nonparametric optimal benchmark that provides an upper bound on the performance of a design under a given scenario. A fundamental assumption of the benchmark is that the investigator can arrange doses in a monotonically increasing toxicity order. While the benchmark can be still applied to combination studies in which not all dose combinations can be ordered, it does not account for the uncertainty in the ordering. In this article, we propose a generalization of the benchmark that accounts for this uncertainty and, as a result, provides a sharper upper bound on the performance. The benchmark assesses how probable the occurrence of each ordering is, given the complete information about each patient. The proposed approach can be applied to trials with an arbitrary number of endpoints with discrete or continuous distributions. We illustrate the utility of the benchmark using recently proposed dose-finding designs for Phase I combination trials with a binary toxicity endpoint and Phase I/II combination trials with binary toxicity and continuous efficacy.
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