Modified Toxicity Probability Interval Design: A Safer and More Reliable Method Than the 3 + 3 Design for Practical Phase I Trials

Abstract

The 3 + 3 design is the most common choice among clinicians for phase I dose-escalation oncology trials. In recent reviews, more than 95% of phase I trials have been based on the 3 + 3 design. Given that it is intuitive and its implementation does not require a computer program, clinicians can conduct 3 + 3 dose escalations in practice with virtually no logistic cost, and trial protocols based on the 3 + 3 design pass institutional review board and biostatistics reviews quickly. However, the performance of the 3 + 3 design has rarely been compared with model-based designs in simulation studies with matched sample sizes. In the vast majority of statistical literature, the 3 + 3 design has been shown to be inferior in identifying true maximum-tolerated doses (MTDs), although the sample size required by the 3 + 3 design is often orders-of-magnitude smaller than model-based designs. In this article, through comparative simulation studies with matched sample sizes, we demonstrate that the 3 + 3 design has higher risks of exposing patients to toxic doses above the MTD than the modified toxicity probability interval (mTPI) design, a newly developed adaptive method. In addition, compared with the mTPI design, the 3 + 3 design does not yield higher probabilities in identifying the correct MTD, even when the sample size is matched. Given that the mTPI design is equally transparent, costless to implement with free software, and more flexible in practical situations, we highly encourage its adoption in early dose-escalation studies whenever the 3 + 3 design is also considered. We provide free software to allow direct comparisons of the 3 + 3 design with other model-based designs in simulation studies with matched sample sizes.

A Bayesian interval dose-finding design addressingOckham's razor: mTPI-2

“…Later, in Ji et al (2007Ji et al ( , 2010 and Ji and Wang (2013), the authors further developed toxicity probability interval (TPI) and modified TPI (mTPI) methods, in which they formally proposed a decision theoretic framework linking the dose-finding decisions of "Stay" (S), "De-escalation" (D), and "Escalation" (E) with the equivalence interval EI = (p T − 1 , p T + 2 ), over-dosing interval OI = (p T + 2 , 1), and under-dosing interval U I = (0, p T − 1 ), respectively. For a given dose d, the authors calculate P r(p d ∈ EI | data), P r(p d ∈ OI | data), and P r(p d ∈ U I | data), three posterior probabilities that the toxicity rate p d belongs to each of the three dosing intervals.…”

A Bayesian adaptive design for cancer phase I trials using a flexible range of doses

“…If this dose happens to be too toxic, then the DE design is superior relative to the NDE and NS designs as discussed above. Although the methodology described here uses EWOC, it can be applied to any model based dose finding method such as CRM (O’Quigley et al, 1990) or mTPI (Ji and Wang, 2013) designs. We plan to adapt the methodology to dose combination trials by extending the work described in (Tighiouart, Li and Rogatko, 2017).…”

Phase I dose escalation trial of nitroglycerin in addition to 5-fluorouracil and radiation therapy for neoadjuvant treatment of operable rectal cancer

“…21 All patients received radiation therapy, 45-50 Gy in 25-28 fractions to the pelvis along with continuous infusion of 5-fluorouracil (5-FU) 225 mg/m 2 per day for the duration of the radiation therapy. The radiation therapy was planned and delivered as per institutional standard of care for the Dallas VAMC radiation oncology department.…”