A Bayesian interval dose-finding design addressingOckham's razor: mTPI-2

Guo, Wentian; Wang, Sue‐Jane; Yang, Shengjie; Lynn, Henry; Ji, Yuan

doi:10.1016/j.cct.2017.04.006

Cited by 97 publications

(104 citation statements)

References 18 publications

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“…We studied the trial conduct of the Bayesian optimal interval and the Keyboard methods. The Keyboard method was developed as an improvement to the modified toxicity probability interval method and is equivalent to the modified toxicity probability interval‐2 method . Therefore, we chose not to separately examine the modified toxicity probability interval method since we are studying an equivalent method to the modified toxicity probability interval‐2 method.…”

Section: Methodsmentioning

confidence: 99%

“…The Keyboard method was developed as an improvement to the modified toxicity probability interval method 6 and is equivalent to the modified toxicity probability interval-2 method. 9 Therefore, we chose not to separately examine the modified toxicity probability interval method since we are studying an equivalent method to the modified toxicity probability interval-2 method. The details of the Bayesian optimal interval and the Keyboard methods are provided elsewhere, so we only briefly recall them here.…”

Section: Interval-based Dose Findingmentioning

confidence: 99%

“…But after observing a non-dose-limiting toxicity outcome, it is no longer safe to be at dose level 3 and that the trial must deescalate to dose level 2. Overall, data observed for 13 of 36 (36%) patients accrued to the study (patients 7,9,11,13,14,17,19,21,25,26,29,31, and 33) resulted in an incoherent dose assignment for the following patient accrued to the study for both methods. Incoherent dose assignments are indicated in red in Figure 2.…”

Section: Single Trial Illustrationmentioning

confidence: 99%

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Coherence principles in interval‐based dose finding

Wages

Iasonos

O’Quigley

et al. 2019

Pharmaceutical Statistics

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This paper studies the notion of coherence in interval‐based dose‐finding methods. An incoherent decision is either (a) a recommendation to escalate the dose following an observed dose‐limiting toxicity or (b) a recommendation to deescalate the dose following a non–dose‐limiting toxicity. In a simulated example, we illustrate that the Bayesian optimal interval method and the Keyboard method are not coherent. We generated dose‐limiting toxicity outcomes under an assumed set of true probabilities for a trial of n=36 patients in cohorts of size 1, and we counted the number of incoherent dosing decisions that were made throughout this simulated trial. Each of the methods studied resulted in 13/36 (36%) incoherent decisions in the simulated trial. Additionally, for two different target dose‐limiting toxicity rates, 20% and 30%, and a sample size of n=30 patients, we randomly generated 100 dose‐toxicity curves and tabulated the number of incoherent decisions made by each method in 1000 simulated trials under each curve. For each method studied, the probability of incurring at least one incoherent decision during the conduct of a single trial is greater than 75%. Coherency is an important principle in the conduct of dose‐finding trials. Interval‐based methods violate this principle for cohorts of size 1 and require additional modifications to overcome this shortcoming. Researchers need to take a closer look at the dose assignment behavior of interval‐based methods when using them to plan dose‐finding studies.

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Section: Methodsmentioning

confidence: 99%

Section: Interval-based Dose Findingmentioning

confidence: 99%

Section: Single Trial Illustrationmentioning

confidence: 99%

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Coherence principles in interval‐based dose finding

Wages

Iasonos

O’Quigley

et al. 2019

Pharmaceutical Statistics

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“…We first give a brief review of the mTPI-2 design (Guo et al, 2017), upon which the PoD-TPI design is anchored. Under the mTPI-2 design, patients are usually enrolled in cohorts, and the first cohort is treated at the lowest dose.…”

Section: Review Of Mtpi-2mentioning

confidence: 99%

PoD-TPI: Probability-of-Decision Toxicity Probability Interval Design to Accelerate Phase I Trials

Zhou

Guo

2019

Stat Biosci

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Cohort-based enrollment can slow down dose-finding trials since the outcomes of the previous cohort must be fully evaluated before the next cohort can be enrolled. This results in frequent suspension of patient enrollment. The issue is exacerbated in recent immune oncology trials where toxicity outcomes can take a long time to observe. We propose a novel phase I design, the probability-of-decision toxicity probability interval (PoD-TPI) design, to accelerate phase I trials. PoD-TPI enables dose assignment in real time in the presence of pending toxicity outcomes. With uncertain outcomes, the dose assignment decisions are treated as a random variable, and we calculate the posterior distribution of the decisions. The posterior distribution reflects the variability in the pending outcomes and allows a direct and intuitive evaluation of the confidence of all possible decisions. Optimal decisions are calculated based on 0-1 loss, and extra safety rules are constructed to enforce sufficient protection from exposing patients to risky doses. A new and useful feature of PoD-TPI is that it allows investigators and regulators to balance the trade-off between enrollment speed and making risky decisions by tuning a pair of intuitive design parameters. Through numerical studies, we evaluate the operating characteristics of PoD-TPI and demonstrate that PoD-TPI shortens trial duration and maintains trial safety and efficiency compared to existing time-to-event designs.

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“…Stage I is a run-in period, in which we escalate the dose along the diagonal of the dose combination matrix to explore the dose combination space quickly and to collect preliminary data for stage II. Specifically, in stage I we make dose escalation decisions based on the mTPI-2 design (Guo et al, 2017). There are three possibilities.…”

Section: Overviewmentioning

confidence: 99%

AAA: Triple Adaptive Bayesian Designs for the Identification of Optimal Dose Combinations in Dual-Agent Dose Finding Trials

Lyu

Zhao

et al. 2018

Journal of the Royal Statistical Society Series C: Applied Statistics

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Summary. 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 allows for all three adaptations at the same time. We find that AAA enhances the chance of finding the optimal dose combinations and shortens the trial duration. A clinical trial is being planned to apply the AAA design and a Web tool is being developed for both statisticians and non-statisticians.

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A Bayesian interval dose-finding design addressingOckham's razor: mTPI-2

Cited by 97 publications

References 18 publications

Coherence principles in interval‐based dose finding

Coherence principles in interval‐based dose finding

PoD-TPI: Probability-of-Decision Toxicity Probability Interval Design to Accelerate Phase I Trials

AAA: Triple Adaptive Bayesian Designs for the Identification of Optimal Dose Combinations in Dual-Agent Dose Finding Trials

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