Overall success rate of a safe and efficacious drug: Results using six phase 1 designs, each followed by standard phase 2 and 3 designs

AIDE: Adaptive intrapatient dose escalation designs to accelerate Phase I clinical trials

Designing Phase I clinical trials is challenging when accrual is slow or sample size is limited. The corresponding key question is: how to efficiently and reliably identify the maximum tolerated dose (MTD) using a sample size as small as possible? We propose model-assisted and model-based designs with adaptive intrapatient dose escalation (AIDE) to address this challenge. AIDE is adaptive in that the decision of conducting intrapatient dose escalation depends on both the patient's individual safety data, as well as other enrolled patient's safety data. When both data indicate reasonable safety, a patient may perform intrapatient dose escalation, generating toxicity data at more than one dose. This strategy not only provides patients the opportunity to receive higher potentially more effective doses, but also enables efficient statistical learning of the dose-toxicity profile of the treatment, which dramatically reduces the required sample size. Simulation studies show that the proposed designs are safe, robust, and efficient to identify the MTD with a sample size that is substantially smaller than conventional interpatient dose escalation designs. Practical considerations are provided and R code for implementing AIDE is available upon request.

An overview of the BOIN design and its current extensions for novel early-phase oncology trials

Bayesian Optimal Interval (BOIN) designs are a class of model-assisted dose-finding designs that can be used in oncology trials to determine the maximum tolerated dose (MTD) of a study drug based on safety or the optimal biological dose (OBD) based on safety and efficacy. BOIN designs provide a complete suite for dose finding in early phase trials, as well as a consistent way to explore different scenarios such as toxicity, efficacy, continuous outcomes, delayed toxicity or efficacy and drug combinations in a unified manner with easy access to software to implement most of these designs. Although built upon Bayesian probability models, BOIN designs are operationally simple in general and have good statistical operating characteristics compared to other dose-finding designs. This review paper describes the original BOIN design and its many extensions, their advantages and limitations, the software used to implement them, and the most suitable situation for use of each of these designs. Published examples of the implementation of BOIN designs are provided in the Appendix.

A comparative study of Bayesian optimal interval (BOIN) design with interval 3+3 (i3+3) design for phase I oncology dose-finding trials

Bayesian optimal interval (BOIN) design is a model-assisted phase I dose-finding design to find the maximum tolerated dose (MTD). The hallmark of the BOIN design is its concise decision rule - making the decision of dose escalation and de-escalation by simply comparing the observed dose-limiting toxicity (DLT) rate at the current dose with a pair of optimal dose escalation and de-escalation boundaries. The interval 3+3 (i3+3) design is a recently proposed algorithm-based dose-finding design based on a similar decision rule with some modifications. The similarity in the appearance of the two designs has caused confusions among practitioners. In this article, we demystify the i3+3 design by elucidating its links with the BOIN design and compare their similarities and differences, as well as pros and cons. We perform comprehensive simulation studies to compare the operating characteristics of the two designs. Our results show that, compared to the algorithm-based i3+3 design, which are characterized by ad hoc and often scientifically and logically incoherent decision rules, the mode-assisted BOIN design is not only simpler, but also statistically more rigorous with better operating characteristics, thus providing a better design choice for phase I oncology trials.

Model-Assisted Designs for Early-Phase Clinical Trials: Simplicity Meets Superiority

Drug development enterprise is struggling because of prohibitively high costs and slow progress. There is urgent need for adoption of novel adaptive designs to improve the efficiency and success of clinical trials. A major barrier is that many conventional designs are inadequate for modern drug development, yet most novel adaptive designs are difficult to understand, require complicated statistical modeling, demand complex computation, and need expensive infrastructure for implementation. The objective of this article is to introduce and review a class of novel adaptive designs, known as model-assisted designs, to remove this barrier and increase the use of novel adaptive designs. Model-assisted designs enjoy superior performance comparable to more complicated, model-based adaptive designs, but their decision rule can be pretabulated and included in the protocol-thus implemented as simply as the conventional designs. We review state-of-the-art model-assisted designs for phase I clinical trials for single-agent, drug-combination and late-onset toxicity scenarios. We also briefly introduce model-assisted designs for phase II trials to handle binary, coprimary endpoints and delayed response. Freely available user-friendly software and trial examples (trialdesign.org) facilitate the adoption of model-assisted designs.

Practical considerations for the implementation of adaptive designs for oncology Phase I dose-finding trials

The traditional 3 + 3 design continues to be commonly used for Phase I dose-finding oncology trials, despite increasing criticisms and development of innovative methods. Unfortunately, it is a challenge to convince principal investigators to use novel designs. The goal of this paper is to persuade researchers to break away from 3 + 3 design and provide potential solutions to better designs and implementation strategy. We reviewed the statistical methods for adaptive Phase I designs. The barriers among all the major components of the implementation team have been emphasized and potential solutions have been discussed. Institutional support to the principal investigators and statistician, as well as to other team members is essential to design and implement adaptive trials in academic medical institutions.