AAA: triple adaptive Bayesian designs for the identification of optimal dose combinations in dual‐agent dose finding trials

@article{Lyu2019AAATA,
  title={AAA: triple adaptive Bayesian designs for the identification of optimal dose combinations in dual‐agent dose finding trials},
  author={Jiaying Lyu and Yuan Ji and Naiqing Zhao and Daniel V T Catenacci},
  journal={Journal of the Royal Statistical Society: Series C (Applied Statistics)},
  year={2019},
  volume={68}
}
We propose a flexible design for the identification of optimal dose combinations in dual‐agent dose finding clinical trials. The design is called AAA, standing for three adaptations: adaptive model selection, adaptive dose insertion and adaptive cohort division. The adaptations highlight the need and opportunity for innovation for dual‐agent dose finding and are supported by the numerical results presented in the proposed simulation studies. To our knowledge, this is the first design that… 

A Bayesian adaptive design for dual-agent phase I-II cancer clinical trials combining efficacy data across stages

TLDR
A novel Bayesian adaptive design that accommodates the change of patient population across stages for dual-agent combinations, where one main objective is to characterise both the toxicity and efficacy pro files.

Combining cytotoxic agents with continuous dose levels in seamless phase I-II clinical trials

TLDR
A two-stage design for the combination of two cytotoxic agents assuming a single patient population across the entire study is proposed, which is safe and yields good operating characteristics across scenarios with power and type-I error rates that are consistent with the values reported by [25, 10].

Modeling synergism in early phase cancer trials with drug combination with continuous dose levels: is there an added value?

TLDR
It is found that not including an interaction term in the model can compromise the safety of the trial and reduce the pointwise reliability of the estimated maximum tolerated dose (MTD) curve.

Designs of Early Phase Cancer Trials with Drug Combinations

References

SHOWING 1-10 OF 38 REFERENCES

A product of independent beta probabilities dose escalation design for dual-agent phase I trials

TLDR
A curve-free (nonparametric) design for a dual-agent trial in which the model parameters are the probabilities of toxicity at each of the dose combinations, and it is relatively trivial for a clinician's prior beliefs or historical information to be incorporated in the model and updating is fast and computationally simple.

A Bayesian dose‐finding design for drug combination clinical trials based on the logistic model

TLDR
Under the proposed design, the posterior estimates of the model parameters continuously update to make the decisions of dose assignment and early stopping, and the design is competitive and outperforms some existing designs.

A two-dimensional biased coin design for dual-agent dose-finding trials

TLDR
This work proposes a two-stage adaptive biased coin design that extends existing methods for single-agent trials to dual-agent dose-finding trials, is competitive with existing designs, and promotes patient safety by limiting patient exposure to toxic combinations whenever possible.

Bayesian dose-finding designs for combination of molecularly targeted agents assuming partial stochastic ordering

TLDR
A dose‐combination‐finding algorithm based only on partial stochastic ordering assumptions for the effects of the combined MTAs and uses isotonic regression to estimate partially stochastically ordered marginal posterior distributions of the efficacy and toxicity probabilities is developed.

TEAMS: Toxicity- and Efficacy-Based Dose-Insertion Design with Adaptive Model Selection for Phase I/II Dose-Escalation Trials in Oncology

TLDR
The new design, TEAMS, achieves great operating characteristics in extensive simulation studies due to its ability to adaptively insert new doses as well as perform model selection during the course of the trial.

Two‐Dimensional Dose Finding in Discrete Dose Space

TLDR
This article proposes a Bayesian design that uses a parsimonious working model for the dose-toxicity relationship and shows that the new design is more effective in identifying the maximum-tolerated combinations than one-dimensional designs applied at each dose level of one of the agents.

Bayesian optimal interval design for dose finding in drug-combination trials

  • R. LinG. Yin
  • Mathematics
    Statistical methods in medical research
  • 2017
TLDR
A Bayesian optimal interval design for dose finding in drug-combination trials is developed and enjoys convergence properties for large samples and the entire dose-finding procedure is nonparametric (model-free), which is thus robust and also does not require the typical “nonparametric” prephase used in model-based designs for drug- Combination trials.

A Bayesian dose finding design for oncology clinical trials of combinational biological agents

TLDR
A novel dose finding algorithm is proposed to encourage sufficient exploration of untried dose combinations in the two‐dimensional space and has desirable operating characteristics in identifying the biologically optimal dose combination under various patterns of dose–toxicity and dose–efficacy relationships.

An adaptive design for identifying the dose with the best efficacy/tolerability profile with application to a crossover dose‐finding study

TLDR
This work presents an efficient adaptive dose‐finding strategy that concentrates patient assignments at and around the dose which has the best efficacy/tolerability profile based on a utility function within the setting of a crossover design.