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Rule-Based Designs Considering Toxicity Alone

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Dose-Finding Designs for Early-Phase Cancer Clinical Trials

Part of the book series: SpringerBriefs in Statistics ((JSSRES))

Abstract

The primary objective of a phase I trial for an anticancer agent is to determine the maximum tolerated dose (MTD), which is defined as the dose having a toxicity (in particular, the dose-limiting toxicity) probability closest to the prespecified target toxicity probability level. Thus, dose-finding designs for phase I trials usually focus on the frequency or incidence of toxicity alone. Traditionally, dose-finding designs can be broadly classified as rule-/algorithm- or model-based designs. Rule-based designs identify the MTD by relying on prespecified dose escalation and de-escalation rules, whereas model-based designs estimate the MTD by fitting a model assumed to reflect the monotonic dose–toxicity relationship to the corresponding data. In this chapter, we focus on rule-based designs. In particular, we describe the \(3+3\) design and its variations, because this is the most common rule-based design used in practice. However, we also emphasize the limitations of the \(3+3\) design, because it poorly identifies the MTD despite its simplicity and transparency. With this in mind, we then overview alternative rule-based designs that can improve the performance of the \(3+3\) design and discuss some related topics.

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Authors and Affiliations

  1. Department of Biostatistics, Hyogo College of Medicine, Nishinomiya, Hyogo, Japan

    Takashi Daimon

  2. Department of Biostatistics and Bioinformatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan

    Akihiro Hirakawa

  3. Graduate School of Medicine, Nagoya University, Nagoya, Aichi, Japan

    Shigeyuki Matsui

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  1. Takashi Daimon
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  2. Akihiro Hirakawa
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  3. Shigeyuki Matsui
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Correspondence to Takashi Daimon .

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Daimon, T., Hirakawa, A., Matsui, S. (2019). Rule-Based Designs Considering Toxicity Alone. In: Dose-Finding Designs for Early-Phase Cancer Clinical Trials. SpringerBriefs in Statistics(). Springer, Tokyo. https://doi.org/10.1007/978-4-431-55585-8_2

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