Revisiting isotonic phase I design in the era of model-assisted dose-finding

Abstract

Background/aims In the conduct of phase I trials, the limited use of innovative model-based designs in practice has led to an introduction of a class of "model-assisted" designs with the aim of effectively balancing the trade-off between design simplicity and performance. Prior to the recent surge of these designs, methods that allocated patients to doses based on isotonic toxicity probability estimates were proposed. Like model-assisted methods, isotonic designs allow investigators to avoid difficulties associated with pre-trial parametric specifications of model-based designs. The aim of this work is to take a fresh look at an isotonic design in light of the current landscape of model-assisted methods. Methods The isotonic phase I method of Conaway, Dunbar, and Peddada was proposed in 2004 and has been regarded primarily as a design for dose-finding in drug combinations. It has largely been overlooked in the single-agent setting. Given its strong simulation performance in application to more complex dose-finding problems, such as drug combinations and patient heterogeneity, as well as the recent development of user-friendly software to accompany the method, we take a fresh look at this design and compare it to a current model-assisted method. We generated operating characteristics of the Conaway-Dunbar-Peddada method using a new web application developed for simulating and implementing the design and compared it to the recently proposed Keyboard design that is based on toxicity probability intervals. Results The Conaway-Dunbar-Peddada method has better performance in terms of accuracy of dose recommendation and safety in patient allocation in 17 of 20 scenarios considered. The Conaway-Dunbar-Peddada method also allocated fewer patients to doses above the maximum tolerated dose than the Keyboard method in many of scenarios studied. Overall, the performance of the Conaway-Dunbar-Peddada method is strong when compared to the Keyboard method, making it a viable simple alternative to the model-assisted methods developed in recent years. Conclusion The Conaway-Dunbar-Peddada method does not rely on the specification and fitting of a parametric model for the entire dose-toxicity curve to estimate toxicity probabilities as other model-based designs do. It relies on a similar set of pre-trial specifications to toxicity probability interval-based methods, yet unlike model-assisted methods, it is able to borrow information across all dose levels, increasing its efficiency. We hope this concise study of the Conaway-Dunbar-Peddada method, and the availability of user-friendly software, will augment its use in practice.

Adapting isotonic dose-finding to a dynamic set of drug combinations with application to a phase I leukemia trial

“…In the absence of prior information, a practical prior specification can be acquired by setting the prior mean equal to the target dose-limiting toxicity rate π * and setting the 95% upper limit u i equal to 2 × π * at each dose level. This prior specification is recommended by Wages and Conaway 22 to avoid the problem of rigidity 23 in which allocation can become confined to a sub-optimal combination regardless of the ensuing observed data. Based on a target dose-limiting toxicity rate of 0.20, we used a Beta ( 2 . 6 , 10 . 4 ) prior for π at each combination.…”

A Brief Overview of Adaptive Designs for Phase I Cancer Trials

“…However, the accuracy of MTDs estimated by isotonic designs are much lower than other model-based designs such as continual reassessment method (CRM) and escalation with overdose control (EWOC; described below) designs. Many studies have applied modifications to isotonic designs for phase I trials [30][31][32]. For example, pool-adjacent-violators algorithm (PAVA) are used in addition to isotonic regression methods to estimate the probabilities of MTDs for each dose level [30].…”

Adaptive dose-finding based on safety and feasibility in early-phase clinical trials of adoptive cell immunotherapy

“…In the absence of prior information, a practical prior specification can be acquired by setting the prior mean equal to the target dose-limiting toxicity rate p à and setting the 95% upper limit u i equal to 2 3 p à at each dose level. This prior specification is recommended by Wages and Conaway 9 to avoid the problem of rigidity 10 in which allocation can become confined to a suboptimal dose level regardless of the ensuing observed data. Based on a target dose-limiting toxicity rate of 25%, we used a Beta (2:34, 7:02) prior for p at each dose level.…”