Bridging across patient subgroups in phase I oncology trials that incorporate animal data

Zheng, Haiyan; Jaki, Thomas

doi:10.1177/0962280220986580

Cited by 3 publications

(5 citation statements)

References 34 publications

(58 reference statements)

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“…Bayesian meta-analytic approaches have been considered to borrow historical data in phase I [ 23 , 24 ] and phase II [ 21 , 26 ] clinical trials. As far as we are aware, this paper represents a very first proposal to using pilot data in the pivotal trial with a crossover design to evaluate bioequivalence.…”

Section: Discussionmentioning

confidence: 99%

“…To enable more effective down weighting of inconsistent pilot data, one may consider transforming the pivotal trial in a two- or multi-stage manner. More specifically, investigators may specify w based on the best guess to initiate the pivotal trial, but reestimate the value at interim analyses for satisfactory operating characteristics in the final analysis [ 24 ].…”

Section: Discussionmentioning

confidence: 99%

“…This robust model can also well accommodate situations of data inconsistency: if drastically different from the treatment effect based on pilot data, can be estimated using the pivotal trial data alone under a weakly-informative prior, . A similar modelling strategy was adopted by Zheng et al [ 23 ] to incorporate preclinical data from one or multiple animal species in the design and analysis of phase I dose-escalation trials and later extended for situations when the phase I trial involves potentially heterogeneous patient subpopulations [ 24 ].…”

Section: Methodsmentioning

confidence: 99%

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A Bayesian approach to pilot-pivotal trials for bioequivalence assessment

Lv,

Grayling,

Zhang

et al. 2023

BMC Med Res Methodol

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Background To demonstrate bioequivalence between two drug formulations, a pilot trial is often conducted prior to a pivotal trial to assess feasibility and gain preliminary information about the treatment effect. Due to the limited sample size, it is not recommended to perform significance tests at the conventional 5% level using pilot data to determine if a pivotal trial should take place. Whilst some authors suggest to relax the significance level, a Bayesian framework provides an alternative for informing the decision-making. Moreover, a Bayesian approach also readily permits possible incorporation of pilot data in priors for the parameters that underpin the pivotal trial. Methods We consider two-sequence, two-period crossover designs that compare test (T) and reference (R) treatments. We propose a robust Bayesian hierarchical model, embedded with a scaling factor, to elicit a Go/No-Go decision using predictive probabilities. Following a Go decision, the final analysis to formally establish bioequivalence can leverage both the pilot and pivotal trial data jointly. A simulation study is performed to evaluate trial operating characteristics. Results Compared with conventional procedures, our proposed method improves the decision-making to correctly allocate a Go decision in scenarios of bioequivalence. By choosing an appropriate threshold, the probability of correctly (incorrectly) making a No-Go (Go) decision can be ensured at a desired target level. Using both pilot and pivotal trial data in the final analysis can result in a higher chance of declaring bioequivalence. The false positive rate can be maintained in situations when T and R are not bioequivalent. Conclusions The proposed methodology is novel and effective in different stages of bioequivalence assessment. It can greatly enhance the decision-making process in bioequivalence trials, particularly in situations with a small sample size.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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A Bayesian approach to pilot-pivotal trials for bioequivalence assessment

Lv,

Grayling,

Zhang

et al. 2023

BMC Med Res Methodol

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“…Consequently, between‐stage heterogeneity and between‐population heterogeneity cannot be disentangled. If our trial would involve multiple populations in each stage, we would need additional random‐effects distributions to account for the between‐population differences (Zheng et al., 2020 , 2021 ). The values of m 0 and R 0 are selected so that they induce weakly informative prior distributions over the parameters in Ψ 2 .…”

Section: Motivating Example and Statistical Modelsmentioning

confidence: 99%

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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“…Consequently, between-stage heterogeneity and between-population heterogeneity cannot be disentangled. If our trial would involve multiple populations in each stage, we would need additional random-effects distributions to account for the between-population differences 26,27 . We define…”

Section: Stage IImentioning

confidence: 99%

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

Jiménez¹,

Zheng²

2021

Preprint

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Integrated phase I-II clinical trial designs are efficient approaches to accelerate drug development. In cases where efficacy cannot be ascertained in a short period of time, two-stage approaches are usually employed. When different patient populations are involved across stages, it is worth of discussion about the use of efficacy data collected from both stages. In this paper, we focus on a two-stage design that aims to estimate safe dose combinations with a certain level of efficacy. In stage I, conditional escalation with overdose control (EWOC) is used to allocate successive cohorts of patients. The maximum tolerated dose (MTD) curve is estimated based on a Bayesian dose-toxicity model. In stage II, we consider an adaptive allocation of patients to drug combinations that have a high probability of being efficacious along the obtained MTD curve. A robust Bayesian hierarchical model is proposed to allow sharing of information on the efficacy parameters across stages assuming 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 proposed methodology is assessed with extensive simulations motivated by a real phase I-II drug combination trial using continuous doses.

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Bridging across patient subgroups in phase I oncology trials that incorporate animal data

Cited by 3 publications

References 34 publications

A Bayesian approach to pilot-pivotal trials for bioequivalence assessment

A Bayesian approach to pilot-pivotal trials for bioequivalence assessment

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

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

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