The Continual Reassessment Method for Multiple Toxicity Grades: A Bayesian Model Selection Approach

Pan, Haitao; Zhu, Cailin; Yuan, Ying; Zhang, Shemin; Zhang, Wenhong; Li, Chanjuan; Xia, Jielai

doi:10.1371/journal.pone.0098147

Cited by 8 publications

(15 citation statements)

References 12 publications

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“…In the interest of patients’ safety, it is quite common for investigators to attribute an AE to the study drug when it is unclear whether the drug is actually the underlying cause (Mukherjee et al ., ). Errors in toxicity attribution is a topic that has been neglected in the statistical literature and current phase I designs assume that the DLT outcome is measured without error (Pan et al ., ). Our proposed design that allows patient‐specific scores for AE attribution provides a conceptual link between the clinical challenges of phase I trials and the statistical complexity of recent model‐based dose escalation algorithms.…”

Section: Discussionmentioning

confidence: 97%

Phase I Designs That Allow for Uncertainty in the Attribution of Adverse Events

Iasonos

O’Quigley

2016

Journal of the Royal Statistical Society Series C: Applied Statistics

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In determining dose limiting toxicities in Phase I studies, it is necessary to attribute adverse events (AE) to being drug related or not. Such determination is subjective and may introduce bias. In this paper, we develop methods for removing or at least diminishing the impact of this bias on the estimation of the maximum tolerated dose (MTD). The approach we suggest takes into account the subjectivity in the attribution of AE by using model-based dose escalation designs. The results show that gains can be achieved in terms of accuracy by recovering information lost to biases. These biases are a result of ignoring the errors in toxicity attribution.

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Section: Discussionmentioning

confidence: 97%

Phase I Designs That Allow for Uncertainty in the Attribution of Adverse Events

Iasonos

O’Quigley

2016

Journal of the Royal Statistical Society Series C: Applied Statistics

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“…This section briefly reviews the phase I design methods proposed by Yuan,et al and Pan,et al [11,14], Mu, et al [13], and Ivanova, et al [9], respectively. These designs were proposed for the dose-finding trials with non-binary toxicity outcomes.…”

Section: Methodsmentioning

confidence: 99%

“…In the Unified Dose Finding package, three methods are included: The Quasi-CRM and Robust Quasi-CRM design [11,14], a model-assisted unified design-the gBOIN [13], and a model-based unified approach in [9] (denoted by Ivanova design later).…”

Section: Introductionmentioning

confidence: 99%

An R Package Unified Dose Finding for Continuous and Ordinal Outcomes in Phase I Dose-Finding Trials

Pan¹,

Chai-Wei²,

Mu³

et al. 2021

Int J Clin Biostat Biom

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Background and objective: Phase I dose-finding trials are essential in drug development. By finding the maximum tolerated dose (MTD) of a new drug or treatment, a Phase I trial establishes the recommended doses for later-phase testing. The primary toxicity endpoint of interest is often a binary variable, to describe the event of a patient who experiences a dose-limiting toxicity. However, there is a growing interest in dose-finding studies involving non-binary outcomes, defined by either the weighted sum of rates of various toxicity grades or a continuous outcome. Although several novel methods have been proposed in the literature, it still lacks accessible software to implement these methods. Methods: We introduce a newly developed R package, Unified Dose Finding, which implements three phase I dose-finding methods with non-binary outcomes by Yuan, et al. (2007) and Pan, et al. (2014) (Quasi-and Robust Quasi-CRM design), by Mu, et al. (2019) (gBOIN design), and by Ivanova, et al. (2009) (denoted by Ivanova design).Results: For each of the methods, Unified Dose Finding provides corresponding functions that begin with next_ to determine the dose for the next cohort of patients, begin with select_ to select the MTD defined by non-binary toxicity endpoint when the trial is completed, and begin with get_oc to obtain the operating characteristics. Conclusion:The R package Unified Dose Finding provides a user-friendly tool to facilitate the implementation of innovative dose-finding studies with non-binary outcomes.

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“…For single agent dose finding trials in cancer, many authors have investigated properties of statistical models and designs that account for all toxicity grades experienced by patients in the trial. Some of these use multivariable models for eliciting the different grades of toxicities as a function of dose [ 2 – 8 ] and others proposed summary indexes to account for different types of toxicities using weights defined by clinicians [ 9 – 16 ]. In general, there is a modest gain in safety and efficiency of the trial under some scenarios.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Adaptive Design in Cancer Phase I Trials Using Dose Combinations with Ordinal Toxicity Grades

Diniz

Kim

Tighiouart

2020

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We propose a Bayesian adaptive design for early phase drug combination cancer trials incorporating ordinal grade of toxicities. Parametric models are used to describe the relationship between the dose combinations and the probabilities of the ordinal toxicities under the proportional odds assumption. Trial design proceeds by treating cohorts of two patients simultaneously receiving different dose combinations. Specifically, at each stage of the trial, we seek the dose of one agent by minimizing the Bayes risk with respect to a loss function given the current dose of the other agent. We consider two types of loss functions corresponding to the Continual Reassessment Method (CRM) and Escalation with Overdose Control (EWOC). At the end of the trial, we estimate the MTD curve as a function of Bayes estimates of the model parameters. We evaluate design operating characteristics in terms of safety of the trial and percent of dose recommendation at dose combination neighborhoods around the true MTD by comparing this design to the one that uses a binary indicator of DLT. The methodology is further adapted to the case of a pre-specified discrete set of dose combinations.

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The Continual Reassessment Method for Multiple Toxicity Grades: A Bayesian Model Selection Approach

Cited by 8 publications

References 12 publications

Phase I Designs That Allow for Uncertainty in the Attribution of Adverse Events

Phase I Designs That Allow for Uncertainty in the Attribution of Adverse Events

An R Package Unified Dose Finding for Continuous and Ordinal Outcomes in Phase I Dose-Finding Trials

A Bayesian Adaptive Design in Cancer Phase I Trials Using Dose Combinations with Ordinal Toxicity Grades

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