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

Zhou, Tianjian; Guo, Wentian; Ji, Yuan

doi:10.1007/s12561-019-09264-0

Cited by 14 publications

(15 citation statements)

References 23 publications

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“…In the presence of competing discontinuations precluding complete observation of the toxicity outcomes, iSurv-CRM clearly outperformed the TITE-CRM for selecting the correct dose, expectedly when informative censoring was high. Additional performance comparisons with designs other than the TITE-CRM also assuming a time-to-event endpoint such as the PoD-TPI recently developed by Zhou et al (2020) could also be considered in further studies.…”

Section: Discussionmentioning

confidence: 99%

Dose-finding design and benchmark for a right censored endpoint

Andrillon

Chevret

Lee

et al. 2020

Journal of Biopharmaceutical Statistics

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Dose-finding trials aim to determine a safe dose to be tested in larger trials for efficacy. In oncology, designs generally assume conventional monotonic increasing dose-toxicity relationships, mostly with binary outcomes (e.g., dose-limiting toxicity or not), measured in the first cycle of therapy or for a fixed number of cycles. However, with new anti-cancer agents such as molecularly targeted therapies and immunotherapies, late-onset toxicities have become more frequent. Designs with prolonged observation windows and censored endpoints analyzed using survival models, appear particularly suited to these settings. Moreover, in this setting, the observation of the lateonset toxicity endpoint could be precluded by trial discontinuation due to death, progression, patient withdrawal, or physician discretion, defining a competing event to toxicity. We propose extensions of the Continual Reassessment Method (CRM) dose-finding design using survival working models for right-censored endpoints and for handling treatment discontinuation in the toxicity observation window, namely the Survival-CRM (Surv-CRM) and the informative survival-CRM (iSurv-CRM). We also developed a benchmark approach for its evaluation. In a simulation study, we compared the performance of the Surv-CRM and iSurv-CRM, to those of the Time-toevent (TITE)-CRM and the nonparametric benchmark. The performance of the proposed methods was consistent with the complexity of scenarios as assessed by the nonparametric benchmark. Without treatment discontinuations, the Surv-CRM provides proportions of correct dose selection close to those of the TITE-CRM with fewer observed toxicities and patients assigned to overtoxic dose levels. In the presence of treatment discontinuation, the iSurv-CRM outperforms the TITE-CRM in identifying the correct dose level.

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Section: Discussionmentioning

confidence: 99%

Dose-finding design and benchmark for a right censored endpoint

Andrillon

Chevret

Lee

et al. 2020

Journal of Biopharmaceutical Statistics

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“…The rolling TPI (R-TPI) design is an extension of the mTPI-2 design with a fixed set of decision rules to accommodate pending outcomes (Guo et al, 2019). Recently, in the probability-of-decision toxicity probability interval design (PoD-TPI) by Zhou et al (2020), the dose assignment decision is treated as a random variable, and the posterior distribution of the decisions is computed to accommodate uncertainties in pending outcomes.…”

Section: Existing Approaches On Time-to-toxicity and Multi-grade Toxi...mentioning

confidence: 99%

“…We follow the idea of the PoD-TPI design (Zhou et al, 2020) for decision-making. Specifically, the observed time-to-event data can be used to predict the probability of a pending patient experiencing MT or DLT at the end of follow-up, under which we can compute the probability of possible dose allocation decisions.…”

Section: Posterior Predictive Imputationmentioning

confidence: 99%

PoD-BIN: A Probability of Decision Bayesian Interval Design for Time-to-Event Dose-Finding Trials with Multiple Toxicity Grades

Liu,

Ji,

Lin

2021

Preprint

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We consider a Bayesian framework based on "probability of decision" for dose-finding trial designs. The proposed PoD-BIN design evaluates the posterior predictive probabilities of up-and-down decisions. In PoD-BIN, multiple grades of toxicity, categorized as the mild toxicity (MT) and dose-limiting toxicity (DLT), are modeled simultaneously, and the primary outcome of interests is time-to-toxicity for both MT and DLT. This allows the possibility of enrolling new patients when previously enrolled patients are still being followed for toxicity, thus potentially shortening trial length. The Bayesian decision rules in PoD-BIN utilize the probability of decisions to balance the need to speed up the trial and the risk of exposing patients to overly toxic doses. We demonstrate via numerical examples the resulting balance of speed and safety of PoD-BIN and compare to existing designs.

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“…22 Zhou proposed the probability-of-decision toxicity probability interval design to accelerate the phase I trials which enabled the real-time dose assignment in the presence of pending toxicity by calculating the posterior distribution of the decisions. 23 Although these designs can handle late-onset outcomes, they are based solely on binary toxicity outcomes without considering different toxicity grades.…”

Section: Introductionmentioning

confidence: 99%

TITE‐gBOIN: Time‐to‐event Bayesian optimal interval design to accelerate dose‐finding accounting for toxicity grades

Takeda

Xia

Liu

et al. 2021

Pharmaceutical Statistics

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The new therapeutic agents, such as molecular targeted agents and immunooncology therapies, appear more likely to induce multiple toxicities at different grades than dose-limiting toxicities defined in traditional dose-finding trials. In addition, it is often challenging to make adaptive decisions on dose escalation and de-escalation on time because of the fast accrual rate and/or the late-onset toxicity outcomes, causing the potential suspension of the enrollment and the delay of the trials. To address these issues, we propose a time-to-event Bayesian optimal interval design to accelerate the dose-finding process utilizing toxicity grades based on both cumulative and pending toxicity outcomes. The proposed design, named "TITE-gBOIN" design, is a nonparametric and modelassisted design and has the virtues of robustness, simplicity and straightforward to implement in actual oncology dose-finding trials. A simulation study shows that the TITE-gBOIN design has a higher probability of selecting the MTDs correctly and allocating more patients to the MTDs across various realistic settings while reducing the trial duration significantly, therefore can accelerate early-stage dose-finding trials.

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

Cited by 14 publications

References 23 publications

Dose-finding design and benchmark for a right censored endpoint

Dose-finding design and benchmark for a right censored endpoint

PoD-BIN: A Probability of Decision Bayesian Interval Design for Time-to-Event Dose-Finding Trials with Multiple Toxicity Grades

TITE‐gBOIN: Time‐to‐event Bayesian optimal interval design to accelerate dose‐finding accounting for toxicity grades

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