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Adaptive Bayesian Designs for Dose-Ranging Drug Trials

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Part of the book series: Lecture Notes in Statistics ((LNS,volume 162))

Abstract

In the standard type of phase II efficacy trial, patients are assigned to a dose from among those being considered (usually 4 to 12 in number). Assignment is random, usually with equal numbers of patients assigned to each dose. Based on the results of the trial, a decision is made to either enter phase III in the drug’s development, stop the drug’s development, or conduct another phase II trial. Such a design is inefficient, in terms of both time and resources. We have developed an innovative class of designs that we are introducing into practice. In this case study we describe the designs, address difficulties in implementing them in actual clinical trials, and relay our experience with using them.

The first stage allows for a wide range and a large number of doses, including placebo. The purpose of this stage is to assess dose-response in an informative and efficient way. Assignment to dose is sequential, in the following sense. As patients are treated, they are followed and their responses are communicated to a central database. Doses are assigned to subsequent patients so as to obtain maximal and rapid information about the dose-response curve. We impute missing final responses using their predictive distributions given current responses and given assigned doses.

As information accrues about dose-response from the dose-finding stage, if this information is suggestive that the drug is effective then the assignment procedure shifts to a confirmatory stage (“pivotal” phase III). Two doses will be identified based on the dose-response information and patients will be randomized to these two doses and placebo in a balanced fashion. The shift will be seamless and not recognizable by physicians and others involved in the trial (except for members of the trial’s data and safety monitoring committee).

The timing of the shift from dose-finding to pivotal is critical. Whether to shift will be based on a Bayesian decision analysis using forward simulation and dynamic programming. A decision to shift will depend on the available information about dose-response, the costs of entering additional patients, and the requirements of the FDA and other regulatory agencies concerning information needed for eventual marketing approval of the drug. Although the design and the determination of the sample size of the pivotal stage is Bayesian, the decision analysis recognizes the need to provide regulatory agencies with a frequentist analysis of the trial results.

Our efficient dose assignment scheme more accurately identifies effective drugs and it more accurately identifies ineffective drugs. Moreover, efficient dose assignment can significantly shorten a drug’s clinical development. First, the number of patients in a sequential trial will usually be substantially smaller than when using standard designs. This has important economical and ethical implications. Second, a seamless transition between the dose-finding and confirmatory stages eliminates the time required to set up a second trial.

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Berry, D.A. et al. (2002). Adaptive Bayesian Designs for Dose-Ranging Drug Trials. In: Gatsonis, C., et al. Case Studies in Bayesian Statistics. Lecture Notes in Statistics, vol 162. Springer, New York, NY. https://doi.org/10.1007/978-1-4613-0035-9_2

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  • DOI: https://doi.org/10.1007/978-1-4613-0035-9_2

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-95169-0

  • Online ISBN: 978-1-4613-0035-9

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