Bayesian decision procedures for binary and continuous bivariate dose‐escalation studies

Zhou, Yinghui; Whitehead, John; Bonvini, Elisa; Stevens, John

doi:10.1002/pst.222

Cited by 36 publications

(33 citation statements)

References 19 publications

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“…Some recently proposed designs for phase I clinical trials consider both toxicity and efficacy (Bekele and Shen, 2005;Braun, 2002;Dragalin and Fedorov, 2006;Dragalin et al, 2008;Gooley et al, 1994;Loke et al, 2006;O'Quigley et al, 2001;Thall and Russell, 1998;Thall and Cook, 2004;Thall et al, 1999;Zhou et al, 2006). The statistical model for the joint occurrence of response and toxicity varies, but in most of these approaches there occurs Bayesian updating of priors through the data.…”

Section: Wang and Daymentioning

confidence: 98%

“…The proposed design is built on a joint model for thresholds for doses causing response and toxicity, leading to a probability model for the bivariate binary outcome. Previous methods (Bekele and Shen, 2005;Braun, 2002;Dragalin and Fedorov, 2006;Loke et al, 2006;O'Quigley et al, 2001;Thall and Cook, 2004;Thall and Russell, 1998;Zhou et al, 2006) model the probabilities themselves. In addition, we consider the utility of each of the bivariate binary outcomes separately.…”

Section: Wang and Daymentioning

confidence: 99%

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Adaptive Bayesian Design for Phase I Dose-Finding Trials Using a Joint Model of Response and Toxicity

Wang

Day

2009

Journal of Biopharmaceutical Statistics

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We present a new adaptive Bayesian method for dose-finding in phase I clinical trials based on both response and toxicity. Although clinical responses are usually rare in phase I cancer trials, molecularly targeted therapy may make clinical responses more likely. In addition, biological responses may be common. Thus responses may be frequent enough to help decide how aggressive a phase I escalation should be. The model assumes that response and toxicity events happen depending on respective dose thresholds for the individual, assuming that the thresholds jointly follow a bivariate log-normal distribution or a mixture. The design utilizes prior information about the population threshold distribution as well as accumulated data. The next dose is assigned to maximize a patient-oriented expected utility integrated over the current posterior distribution. The design is evaluated through simulation with population parameters equaling estimates from early Gleevec trials. This exercise provides evidence for the value of the use of the proposed design for future clinical trials.

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Section: Wang and Daymentioning

confidence: 98%

Section: Wang and Daymentioning

confidence: 99%

Adaptive Bayesian Design for Phase I Dose-Finding Trials Using a Joint Model of Response and Toxicity

Wang

Day

2009

Journal of Biopharmaceutical Statistics

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“…They too imposed constraints to keep a low probability of an administered dose exceeding some maximum acceptable dose level and also extended their approach to adaptive trials. Other notable research includes Whitehead and Williamson (1998); Whitehead et al (2001Whitehead et al ( , 2006a; Zhou et al (2006Zhou et al ( , 2008; Thall et al (2008). A review of recent methods for designing phase I clinical trials was given by Rosenberger and Haines (2002) and, more recently, a book on statistical methods for dose finding by Chevret (2006).…”

Section: Introductionmentioning

confidence: 99%

“…A well-designed experiment should avoid, or at the very least minimize, the risk of administering a toxic dose. The issue of safety has been addressed recently by Zhou et al (2006) and Zhou et al (2008). In both papers, toxicity was controlled by bounding exposure to the drug, that is, not administering a dose if the estimated exposure is larger than a pre-defined tolerance level.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Bayesian compound designs for dose finding studies

McGree

Drovandi

Thompson

et al. 2012

Journal of Statistical Planning and Inference

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This is the author's version of a work that was accepted for publication in Journal of Statistical Planning and Inference. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Statistical Planning and Inference, [VOL 142, ISS 6, (2012 AbstractWe consider the problem of how to efficiently and safely design dose finding studies. Both current and novel utility functions are explored using Bayesian adaptive design methodology for the estimation of a maximum tolerated dose (MTD). In particular, we explore widely adopted approaches such as the continual reassessment method and minimizing the variance of the estimate of an MTD. New utility functions are constructed in the Bayesian framework and are evaluated against current approaches. To reduce computing time, importance sampling is implemented to re-weight posterior samples thus avoiding the need to draw samples using Markov chain Monte Carlo techniques.Further, as such studies are generally first-in-man, the safety of patients is paramount. We therefore explore methods for the incorporation of safety considerations into utility functions to ensure that only safe and well-predicted doses are administered. The amalgamation of Bayesian methodology, adaptive design and compound utility functions is termed adaptive Bayesian compound design (ABCD). The performance of this amalgamation of methodology is investigated via the simulation of dose finding studies. The paper concludes with a discussion of results and extensions that could be included into our approach.

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“…Recent summaries of that methodology can be found in Rosenberger and Haines (2002), Chevret (2006) and Ting (2006). Whitehead et al (2001Whitehead et al ( , 2006a proposed Bayesian dose-escalation procedures for studies of pharmacokinetic responses from repeated dosing of healthy volunteers, Zhou et al (2006) consider escalation based on bivariate data with one continuous and one binary component, and Zhou et al (2007) describe a phase I study in which procedures for monitoring AUC and C max values and incidence of adverse reactions were operated simultaneously. Piantadosi and Liu (1996) describe a dose-escalation procedure based on binary responses of toxicity in which pharmacokinetic measurements are taken into account.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Approach for Dose-Escalation in a Phase I Clinical Trial Incorporating Pharmacodynamic Endpoints

Whitehead

Zhou

Ledent

et al. 2007

Journal of Biopharmaceutical Statistics

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Bayesian decision procedures have already been proposed for and implemented in phase I dose-escalation studies in healthy volunteers. The procedures have been based on pharmacokinetic responses reflecting the concentration of the drug in blood plasma and are conducted to learn about the dose-response relationship while avoiding excessive concentrations.However, in many dose-escalation studies, pharmacodynamic endpoints such as heart rate or blood pressure are observed, and it is these that should be used to control dose-escalation. These endpoints introduce additional complexity into the modelling of the problem relative to pharmacokinetic responses. Firstly, there are responses available following placebo administrations.Secondly, the pharmacodynamic responses are related directly to measurable plasma concentrations, which in turn are related to dose. Motivated by experience of data from a real study conducted in a conventional manner, this paper presents and evaluates a Bayesian procedure devised for the simultaneous monitoring of pharmacodynamic and pharmacokinetic responses. Account is also taken of the incidence of adverse events. Following logarithmic transformations, a linear model is used to relate dose to the pharmacokinetic endpoint and a quadratic model to relate the latter to the pharmacodynamic endpoint. A logistic model is used to relate the pharmacokinetic endpoint to the risk of an adverse event.

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Bayesian decision procedures for binary and continuous bivariate dose‐escalation studies

Cited by 36 publications

References 19 publications

Adaptive Bayesian Design for Phase I Dose-Finding Trials Using a Joint Model of Response and Toxicity

Adaptive Bayesian Design for Phase I Dose-Finding Trials Using a Joint Model of Response and Toxicity

Adaptive Bayesian compound designs for dose finding studies

A Bayesian Approach for Dose-Escalation in a Phase I Clinical Trial Incorporating Pharmacodynamic Endpoints

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