ArticleContemporary clinical trials communications2017
Systematic comparison of the statistical operating characteristics of various Phase I oncology designs.
Article in Contemporary clinical trials communications, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- First-in-Human Study of [Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2025Trial
- DOSET: dose optimization with simultaneous efficacy and toxicity evaluation, motivated by a first-in-human phase I CAR T lymphoma trial.ESMO open · 2026Article
- Phase 1b Study of Tovorafenib, Plozalizumab or Vedolizumab Plus Standard-of-Care Immune Checkpoint Inhibitors in Patients with Advanced Melanoma.Journal of Cancer · 2025Article
- Phase I CAR-T Clinical Trials Review.Anticancer research · 2022Review
- Healthy volunteers in first-in-human oncology drug development for small molecules.British journal of clinical pharmacology · 2022Review
- Operating characteristics are needed to properly evaluate the scientific validity of phase I protocols.Contemporary clinical trials · 2021Article
- Bayesian optimization for estimating the maximum tolerated dose in Phase I clinical trials.Contemporary clinical trials communications · 2021Article
- 2D (2 Dimensional) TEQR design for Determining the optimal Dose for safety and efficacy.Contemporary clinical trials communications · 2019Article
- The Impact of Early-Phase Trial Design in the Drug Development Process.Clinical cancer research : an official journal of the American Association for Cancer Research · 2019Article
- Extensions of the mTPI and TEQR designs to include non-monotone efficacy in addition to toxicity for optimal dose determination for early phase immunotherapy oncology trials.Contemporary clinical trials communications · 2018Article
- A Bayesian adaptive design for cancer phase I trials using a flexible range of doses.Journal of biopharmaceutical statistics · 2018Article
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6 authors.
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Abstract
Dose finding Phase I oncology designs can be broadly categorized as rule based, such as the 3 + 3 and the accelerated titration designs, or model based, such as the CRM and Eff-Tox designs. This paper systematically reviews and compares through simulations several statistical operating characteristics, including the accuracy of maximum tolerated dose (MTD) selection, the percentage of patients assigned to the MTD, over-dosing, under-dosing, and the trial dose-limiting toxicity (DLT) rate, of eleven rule-based and model-based Phase I oncology designs that target or pre-specify a DLT rate of ∼0.2, for three sets of true DLT probabilities. These DLT probabilities are generated at common dosages from specific linear, logistic, and log-logistic dose-toxicity curves. We find that all the designs examined select the MTD much more accurately when there is a clear separation between the true DLT rate at the MTD and the rates at the dose level immediately above and below it, such as for the DLT rates generated using the chosen logistic dose-toxicity curve; the separations in these true DLT rates depend, in turn, not only on the functional form of the dose-toxicity curve but also on the investigated dose levels and the parameter set-up. The model based mTPI, TEQR, BOIN, CRM and EWOC designs perform well and assign the greatest percentages of patients to the MTD, and also have a reasonably high probability of picking the true MTD across the three dose-toxicity curves examined. Among the rule-based designs studied, the 5 + 5 a design picks the MTD as accurately as the model based designs for the true DLT rates generated using the chosen log-logistic and linear dose-toxicity curves, but requires enrolling a higher number of patients than the other designs. We also find that it is critical to pick a design that is aligned with the true DLT rate of interest. Further, we note that Phase I trials are very small in general and hence may not provide accurate estimates of the MTD. Thus our work provides a map for planning Phase I oncology trials or developing new ones.
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