3066 Background: Other than the 3+3 design, new model-based statistical designs like the mTPI design (Ji and Wang, 2013, JCO) are alternative choices for oncology dose-finding trials, including immune oncology dose-finding trials (Atkins et al., 2018, Lancet Oncology). One major criticism of the 3+3 design is that it is based on simple rules, does not depend on statistical models for inference, and leads to unsafe and unreliable operating characteristics. However, the rule-based nature allows 3+3 to be easily understood and implemented in practice, making it practically attractive and friendly. Can friendly rule-based designs achieve great performance seen in model-based designs? For four decades, the answer has been NO. Methods: We propose a new rule-based design called i3+3, where the letter "i" represents the word "interval". The i3+3 design is based on simple but more clever rules that account for the variabilities in the observed data. In short, the i3+3 design simply asks clinicians to compare observed toxicity rates with a prespecified toxicity interval, and make dose escalation decisions according to three simple rules. No sophisticated modeling is needed and the entire design is transparent to clinicians. Results: We compare the operating characteristics for the proposed i3+3 design with other popular phase I designs by simulation. The i3+3 design is far superior than the 3+3 design in trial safety and the ability to identify the true MTD. Compared with model-based phase I designs, i3+3 also demonstrates comparable performances. In other words, the i3+3 design possesses both simplicity and transparency of the rule-based approaches, and the superior operating characteristics seen in model-based approaches. An online R Shiny tool is provided to illustrate the i3+3 design, although in practice it requires no software to design or conduct a dose-finding trial using the design. Conclusions: The i3+3 design could be a practice-altering method for the clinical community. It may increase the safety and efficiency of dose finding trials.
Embracing model-based designs for dose-finding trials