Early Completion of Model-Assisted Designs for Dose-Finding Trials

Application of multi-armed bandits to dose-finding clinical designs

Multi-armed bandits are very simple and powerful methods to determine actions to maximize a reward in a limited number of trials. An early phase in dose-finding clinical trials needs to identify the maximum tolerated dose among multiple doses by repeating the dose-assignment. We consider applying the superior selection performance of multi-armed bandits to dose-finding clinical designs. Among the multi-armed bandits, we first consider the use of Thompson sampling which determines actions based on random samples from a posterior distribution. In the small sample size, as shown in dose-finding trials, because the tails of posterior distribution are heavier and random samples are too much variability, we also consider an application of regularized Thompson sampling and greedy algorithm. The greedy algorithm determines a dose based on a posterior mean. In addition, we also propose a method to determine a dose based on a posterior mode. We evaluate the performance of our proposed designs for nine scenarios via simulation studies.

Data-dependent early completion of dose-finding trials for drug-combination

PURPOSE

Model-assisted designs for drug combination trials have been proposed as novel designs with simple and superior performance. However, model-assisted designs have the disadvantage that the sample size must be set in advance, and trials cannot be completed until the number of patients treated reaches the pre-set sample size. Model-assisted designs have a stopping rule that can be used to terminate the trial if the number of patients treated exceeds the predetermined number, there is no statistical basis for the predetermined number. Here, I propose two methods for data-dependent early completion of dose-finding trials for drug combination: (1) an early completion method based on dose retainment probability, and (2) an early completion method in which the dose retainment probability is adjusted by a bivariate isotonic regression.

METHODS

Early completion is determined when the dose retainment probability using both trial data and the number of remaining patients is high. Early completion of a virtual trial was demonstrated. The performances of the early completion methods were evaluated by simulation studies with 12 scenarios.

RESULTS

The simulation studies showed that the percentage of early completion was an average of approximately 70%, and the number of patients treated was 25% less than the planned sample size. The percentage of correct maximum tolerated dose combination selection for the early completion methods was similar to that of non-early completion methods with an average difference of approximately 3%.

CONCLUSION

The performance of the proposed early completion methods was similar to that of the non-early completion methods. Furthermore, the number of patients for determining early completion before the trial starts was determined and a program code for calculating the dose retainment probability was proposed.

Adaptive design for identifying maximum tolerated dose early to accelerate dose-finding trial

Purpose

The early identification of maximum tolerated dose (MTD) in phase I trial leads to faster progression to a phase II trial or an expansion cohort to confirm efficacy.

Methods

We propose a novel adaptive design for identifying MTD early to accelerate dose-finding trials. The early identification of MTD is determined adaptively by dose-retainment probability using a trial data via Bayesian analysis. We applied the early identification design to an actual trial. A simulation study evaluates the performance of the early identification design.

Results

In the actual study, we confirmed the MTD could be early identified and the study period was shortened. In the simulation study, the percentage of the correct MTD selection in the early identification Keyboard and early identification Bayesian optimal interval (BOIN) designs was almost same from the non-early identification version. The early identification Keyboard and BOIN designs reduced the study duration by about 50% from the model-assisted designs. In addition, the early identification Keyboard and BOIN designs reduced the study duration by about 20% from time-to-event model-assisted designs.

Conclusion

We proposed the early identification of MTD maintaining the accuracy to be able to short the study period.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01584-y.