A NOVEL FRAMEWORK TO ESTIMATE MULTIDIMENSIONAL MINIMUM EFFECTIVE DOSES USING ASYMMETRIC POSTERIOR GAIN AND ϵ-TAPERING

Applications of the partial-order continual reassessment method in the early development of treatment combinations

Combination therapy is increasingly being explored as a promising approach for improving cancer treatment outcomes. However, identifying effective dose combinations in early oncology drug development is challenging due to limited sample sizes in early-phase clinical trials. This task becomes even more complex when multiple agents are being escalated simultaneously, potentially leading to a loss of monotonic toxicity order with respect to the dose. Traditional single-agent trial designs are insufficient for this multi-dimensional problem, necessitating the development and implementation of dose-finding methods specifically designed for drug combinations. While, in practice, approaches to this problem have focused on preselecting combinations with a known toxicity order and applying single-agent designs, this limits the number of combinations considered and may miss promising dose combinations. In recent years, several novel designs have been proposed for exploring partially ordered drug combination spaces with the goal of identifying a maximum tolerated dose combination, based on safety, or an optimal dose combination, based on toxicity and efficacy. However, their implementation in clinical practice remains limited. In this article, we describe the application of the partial order continual reassessment method and its extensions for combination therapies in early-phase clinical trials. We present completed trials that use safety endpoints to identify maximum tolerated dose combinations and adaptively use both safety and efficacy endpoints to determine optimal treatment strategies. We discuss the effectiveness of the partial-order continual reassessment method and its extensions in identifying optimal treatment strategies and provide our experience with executing these novel adaptive designs in practice. By utilizing innovative dose-finding methods, researchers and clinicians can more effectively navigate the challenges of combination therapy development, ultimately improving patient outcomes in the treatment of cancer.

Monotone response surface of multi-factor condition: estimation and Bayes classifiers

We formulate the estimation of monotone response surface of multiple factors as the inverse of an iteration of partially ordered classifier ensembles. Each ensemble (called PIPE-classifiers) is a projection of Bayes classifiers on the constrained space. We prove the inverse of PIPE-classifiers (iPIPE) exists, and propose algorithms to efficiently compute iPIPE by reducing the space over which optimisation is conducted. The methods are applied in analysis and simulation settings where the surface dimension is higher than what the isotonic regression literature typically considers. Simulation shows iPIPE-based credible intervals achieve nominal coverage probability and are more precise compared to unconstrained estimation.