Weishi Chen | Searcharxiv

arXiv2025

Design specification of Partial Ordering Continual Reassessment Method based on consistency conditions

The study of combinations of drugs/drug-schedules gained increasing attention in various therapeutic areas recently. In oncology, the aim of phase I combination clinical trial is to find the maximum tolerated combination (MTC). Many innovative designs were proposed, among which the Partial Ordering Continual Reassessment Method (POCRM) is increasingly applied due to its simplicity and versatility. The POCRM requires specification of plausible monotonic orderings of combinations. However, the choice remains a major difficulty, especially in trials with many compounds or/and combinations. Practical recommendations are given to select six orderings based on statistical considerations, while simulation studies found the design performs poorly when the MTC is in the middle of the combination grid. We prove that the POCRM under currently recommended orderings can be inconsistent (i.e., cannot achieve 100\% correct selection even under infinite samples) which translates into poor performance under small sample size. Based on the derived consistency conditions, we provide two practical recommendations on how to select orderings for real studies (i) based on plausible combination-toxicity scenarios, (ii) regardless of the possible scenarios. We also provide guidance on how to choose other design parameters based on the asymptotic properties and demonstrate how it improves small sample behaviours.

stat.ME↗

arXiv2024

Partial Ordering Bayesian Logistic Regression Model for Phase I Combination Trials and Computationally Efficient Approach to Operational Prior Specification

Recent years have seen increased interest in combining drug agents and/or schedules. Several methods for Phase I combination-escalation trials are proposed, among which, the partial ordering continual reassessment method (POCRM) gained great attention for its simplicity and good operational characteristics. However, the one-parameter nature of the POCRM makes it restrictive in more complicated settings such as the inclusion of a control group. This paper proposes a Bayesian partial ordering logistic model (POBLRM), which combines partial ordering and the more flexible (than CRM) two-parameter logistic model. Simulation studies show that the POBLRM performs similarly as the POCRM in non-randomised settings. When patients are randomised between the experimental dose-combinations and a control, performance is drastically improved. Most designs require specifying hyper-parameters, often chosen from statistical considerations (operational prior). The conventional "grid search'' calibration approach requires large simulations, which are computationally costly. A novel "cyclic calibration" has been proposed to reduce the computation from multiplicative to additive. Furthermore, calibration processes should consider wide ranges of scenarios of true toxicity probabilities to avoid bias. A method to reduce scenarios based on scenario-complexities is suggested. This can reduce the computation by more than 500 folds while remaining operational characteristics similar to the grid search.

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