Bayesian Adaptive Design for Finding the Maximum Tolerated Sequence of Doses in Multicycle Dose-Finding Clinical Trials.

@article{Lyu2018BayesianAD,
  title={Bayesian Adaptive Design for Finding the Maximum Tolerated Sequence of Doses in Multicycle Dose-Finding Clinical Trials.},
  author={Jiaying Lyu and Emily Curran and Yuan Ji},
  journal={JCO precision oncology},
  year={2018},
  volume={2},
  pages={
          1-19
        }
}
PURPOSE Statistical designs for traditional phase I dose-finding trials consider dose-limiting toxicity in the first cycle of treatment. In reality, patients often go through multiple cycles of treatment and may experience toxicity events in more than one cycle. Therefore, it is desirable to identify the maximum tolerated sequence of three doses across three cycles of treatment. METHODS Motivated by a three-cycle dose-finding clinical trial for a rare cancer with a JAK inhibitor, we proposed… 

Figures and Tables from this paper

DICE: A Bayesian model for early dose finding in phase I trials with multiple treatment courses

A design, called DICE (Dose‐fInding CumulativE), for dose escalation and de‐escalation according to previously observed toxicities, which aims at finding the MTD sequence (MTS), and performs an extensive simulation study comparing this approach to the time‐to‐event continual reassessment method (TITE‐CRM).

Bayesian dose regimen assessment in early phase oncology incorporating pharmacokinetics and pharmacodynamics

The DRtox was developed to be applied at the end of the dose‐escalation stage of an ongoing trial for patients with relapsed or refractory acute myeloid leukemia, and outperformed traditional designs in terms of proportion of correctly selecting the MTD‐regimen.

Innovative trial design in precision oncology.

Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine

The history of AI technology as well as the state of the art of medical AI are introduced, focusing on the field of oncology, where AI is expected to play an important role in realizing the current global trend of precision medicine.

References

SHOWING 1-10 OF 25 REFERENCES

A Bayesian dose‐finding design incorporating toxicity data from multiple treatment cycles

This work proposes to account for longitudinal repeated measures of total toxicity profile over multiple treatment cycles, accounting for cumulative toxicity during dosing-finding, and demonstrates an improvement over the QLCRM when data from multiple cycles were used across all scenarios.

Adaptive Phase I clinical trial design using Markov models for conditional probability of toxicity

An alternative approach allowing an assessment of toxicity from each cycle and dose variations for patient over cycles is presented, and gains in using the Markov model as compared to analyses of a single binary outcome are presented.

Dose-finding in phase I clinical trials based on toxicity probability intervals

A dose-finding design that can be easily understood and implemented by non-statisticians is developed that outperforms the 3 + 3 design and performs comparably to other model-based methods in the literature.

A Phase I Bayesian Adaptive Design to Simultaneously Optimize Dose and Schedule Assignments Both Between and Within Patients

A Phase I clinical trial design is proposed that extends existing approaches to optimize dose and schedule solely between patients by incorporating adaptive variations to dose–schedule assignments within patients as the study proceeds, based on a Bayesian nonmixture cure rate model.

Accelerated titration designs for phase I clinical trials in oncology.

Accelerated titration (i.e., rapid intrapatient drug dose escalation) designs appear to effectively reduce the number of patients who are under-treated, speed the completion of phase I trials, and provide a substantial increase in the information obtained.

Adaptive dose insertion in early phase clinical trials

It is believed that with the added adaptive dose insertion, traditional dose-finding trials will have better chances of locating desirable doses and unnecessary trial suspension due to lack of acceptable doses can be avoided.

Bayesian Dose-Finding in Two Treatment Cycles Based on the Joint Utility of Efficacy and Toxicity

A simulation study of the method is presented, including comparison to two-cycle extensions of the conventional 3 + 3 algorithm, continual reassessment method, and a Bayesian model-based design, and evaluation of robustness.

Adaptive clinical trial designs for phase I cancer studies

It is found that modern statistical literature is replete with novel adaptive designs that have clearly defined objectives and established statistical properties, and are shown to outperform conventional dose finding methods such as the 3+3 design, both in terms of statistical efficiency and in Terms of minimizing the number of patients treated at highly toxic or nonefficacious doses.