A Curve‐Free Method for Phase I Clinical Trials

Gasparini, Mauro; Eisele, Jeffrey R.

doi:10.1111/j.0006-341x.2000.00609.x

Cited by 102 publications

(77 citation statements)

References 11 publications

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“…Though it is similar in spirit to the curve-free method proposed by Gasparini and Eisele (2000), there are several important differences. First, as we will describe later, our design utilizes (weakly informative) subjective priors elicited from physicians, rather than non-informative priors, and thus avoids the rigidity problem of the curve-free method (Cheung, 2002).…”

Section: S Liu and V E Johnsonmentioning

confidence: 99%

A robust Bayesian dose-finding design for phase I/II clinical trials

Liu

Johnson

2015

Biostat

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SUMMARYWe propose a Bayesian phase I/II dose-finding trial design that simultaneously accounts for toxicity and efficacy. We model the toxicity and efficacy of investigational doses using a flexible Bayesian dynamic model, which borrows information across doses without imposing stringent parametric assumptions on the shape of the dose-toxicity and dose-efficacy curves. An intuitive utility function that reflects the desirability trade-offs between efficacy and toxicity is used to guide the dose assignment and selection. We also discuss the extension of this design to handle delayed toxicity and efficacy. We conduct extensive simulation studies to examine the operating characteristics of the proposed method under various practical scenarios. The results show that the proposed design possesses good operating characteristics and is robust to the shape of the dose-toxicity and dose-efficacy curves.

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Section: S Liu and V E Johnsonmentioning

confidence: 99%

A robust Bayesian dose-finding design for phase I/II clinical trials

Liu

Johnson

2015

Biostat

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“…We would aim to treat as many patients as possible at the MTD. The essential nature of the CRM (Gasparini and Eisele, 2000) is:…”

Section: Designs With Memorymentioning

confidence: 99%

Experimental designs for phase I and phase I/II dose-finding studies

O’Quigley

Zohar

2006

Br J Cancer

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We review the rationale behind the statistical design of dose-finding studies as used in phase I and phase I/II clinical trials. We underline what the objectives of such dose-finding studies should be and why the widely used standard design fails to meet any of these objectives. The standard design is a 'memoryless' design and we discuss how this impacts on practical behaviour. Designs introduced over the last two decades can be viewed as designs with memory and we discuss how these designs are superior to memoryless designs. By superior we mean that they require less patients overall, less patients to attain the maximum tolerated dose (MTD), and concentrate a higher percentage of patients at and near to the MTD. We reanalyse some recently published studies in order to provide support to our contention that markedly better results could have been achieved had a design with memory been used instead of a memoryless design.

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“…Gasparini et al (2000) propose a Bayesian scheme, where each subject or cohort is treated with the estimated MTD according to the posterior distribution. Limitations and problems of this method are discussed by O'Quigley (2002) and by Cheung (2002) who shows that, for certain dose-response curves, there is a non-negligible probability of treating each subject with a dose that differs from the MTD.…”

Section: Introductionmentioning

confidence: 99%

The treatment versus experimentation dilemma in dose finding studies

Azriel

Mandel

Rinott

2011

Journal of Statistical Planning and Inference

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Phase I clinical trials are conducted in order to find the maximum tolerated dose (MTD) of a given drug from a finite set of doses. For ethical reasons, these studies are usually sequential, treating patients or group of patients with the best available dose according to the current knowledge. However, it is proved here that such designs, and, more generally, designs that concentrate on one dose from some time on, cannot provide consistent estimators for the MTD unless very strong parametric assumptions hold. We describe a family of sequential designs that treat individuals with one of the two closest doses to the estimated MTD, and prove that such designs, under general conditions, concentrate eventually on the two closest doses to the MTD and estimate the MTD consistently. It is shown that this family contains randomized designs that assign the MTD with probability that approaches 1 as the size of the experiment goes to infinity. We compare several designs by simulations, studying their performances in terms of correct estimation of the MTD and the proportion of individuals treated with the MTD.

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A Curve‐Free Method for Phase I Clinical Trials

Cited by 102 publications

References 11 publications

A robust Bayesian dose-finding design for phase I/II clinical trials

A robust Bayesian dose-finding design for phase I/II clinical trials

Experimental designs for phase I and phase I/II dose-finding studies

The treatment versus experimentation dilemma in dose finding studies

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