Two-Stage Design for Phase I–II Cancer Clinical Trials Using Continuous Dose Combinations of Cytotoxic Agents

Tighiouart, Mourad

doi:10.1111/rssc.12294

Cited by 18 publications

(71 citation statements)

References 33 publications

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“…Note that the operating characteristics of this stage are evaluated using informative prior distributions (see Tighiouart, 2019).…”

Section: Stagementioning

confidence: 99%

“…In this section, we introduce the model to estimate the dose combination with highest TTP. In this manuscript, we use a model with high flexibility in a drug combinations space similar to the one used by Tighiouart (2019). However, working on two dimensions with a high level of flexibility would result in a model with too many parameters.…”

Section: Stagementioning

confidence: 99%

“…One possible concern this model could raise is the that it has a fairly large number of parameters and a relatively low sample size (30 patients in stage 2). However, these sample sizes are not uncommon in the dose finding literature (see, e.g., Tighiouart, 2019; Wages & Conaway, 2014).…”

Section: Stagementioning

confidence: 99%

“…In this article, we extend the work of Tighiouart (2019) by proposing a new two‐stage design for clinical trials with drug combinations with a time to event efficacy endpoint in the second stage and continuous dose combination levels. In the first stage, since the efficacy endpoint is evaluated after three or more cycles of therapy, only binary toxicity data is used to guide the dose escalation.…”

Section: Introductionmentioning

confidence: 96%

“…However, in settings where efficacy is not ascertained in a short period of time, it is frequent to employ two‐stage designs where, a set of maximum tolerated dose combinations is first selected, and then tested for efficacy in a second stage with possibly a different population of patients than the one used in the first stage (see Chen, Zhao, Cui, & Kowalski, 2012; Le Tourneau, Lee, & Siu, 2009; Rogatko, Ghosh, Vidakovic, & Tighiouart, 2008). For drug combination trials, different methodologies for two‐stage designs have been proposed for binary efficacy endpoints (see, e.g., Shimamura, Hamada, Matsui, & Hirakawa, 2018; Tighiouart, 2019; Yuan & Yin, 2011; Zhang & Yuan, 2016) but not for time to event endpoints in the second stage of the trial.…”

Section: Introductionmentioning

confidence: 99%

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A Bayesian seamless phase I–II trial design with two stages for cancer clinical trials with drug combinations

2020

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The use of drug combinations in clinical trials is increasingly common during the last years since a more favorable therapeutic response may be obtained by combining drugs. In phase I clinical trials, most of the existing methodology recommends a one unique dose combination as “optimal,” which may result in a subsequent failed phase II clinical trial since other dose combinations may present higher treatment efficacy for the same level of toxicity. We are particularly interested in the setting where it is necessary to wait a few cycles of therapy to observe an efficacy outcome and the phase I and II population of patients are different with respect to treatment efficacy. Under these circumstances, it is common practice to implement two‐stage designs where a set of maximum tolerated dose combinations is selected in a first stage, and then studied in a second stage for treatment efficacy. In this article we present a new two‐stage design for early phase clinical trials with drug combinations. In the first stage, binary toxicity data is used to guide the dose escalation and set the maximum tolerated dose combinations. In the second stage, we take the set of maximum tolerated dose combinations recommended from the first stage, which remains fixed along the entire second stage, and through adaptive randomization, we allocate subsequent cohorts of patients in dose combinations that are likely to have high posterior median time to progression. The methodology is assessed with extensive simulations and exemplified with a real trial.

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“…Note that the operating characteristics of this stage are evaluated using informative prior distributions (see Tighiouart, 2019).…”

Section: Stagementioning

confidence: 99%

Section: Stagementioning

confidence: 99%

Section: Stagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Bayesian seamless phase I–II trial design with two stages for cancer clinical trials with drug combinations

2020

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A Bayesian adaptive design for dual‐agent phase I–II oncology trials integrating efficacy data across stages

Jiménez

Zheng

2023

Biometrical J

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Combination of several anticancer treatments has typically been presumed to have enhanced drug activity. Motivated by a real clinical trial, this paper considers phase I–II dose finding designs for dual‐agent combinations, where one main objective is to characterize both the toxicity and efficacy profiles. We propose a two‐stage Bayesian adaptive design that accommodates a change of patient population in‐between. In stage I, we estimate a maximum tolerated dose combination using the escalation with overdose control (EWOC) principle. This is followed by a stage II, conducted in a new yet relevant patient population, to find the most efficacious dose combination. We implement a robust Bayesian hierarchical random‐effects model to allow sharing of information on the efficacy across stages, assuming that the related parameters are either exchangeable or nonexchangeable. Under the assumption of exchangeability, a random‐effects distribution is specified for the main effects parameters to capture uncertainty about the between‐stage differences. The inclusion of nonexchangeability assumption further enables that the stage‐specific efficacy parameters have their own priors. The proposed methodology is assessed with an extensive simulation study. Our results suggest a general improvement of the operating characteristics for the efficacy assessment, under a conservative assumption about the exchangeability of the parameters a priori.

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A dose‐finding design for dual‐agent trials with patient‐specific doses for one agent with application to an opiate detoxification trial

Mozgunov

Cro

Lingford‐Hughes

et al. 2021

Pharmaceutical Statistics

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Two-Stage Design for Phase I–II Cancer Clinical Trials Using Continuous Dose Combinations of Cytotoxic Agents

Cited by 18 publications

References 33 publications

A Bayesian seamless phase I–II trial design with two stages for cancer clinical trials with drug combinations

A Bayesian seamless phase I–II trial design with two stages for cancer clinical trials with drug combinations

A Bayesian adaptive design for dual‐agent phase I–II oncology trials integrating efficacy data across stages

A dose‐finding design for dual‐agent trials with patient‐specific doses for one agent with application to an opiate detoxification trial

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