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Phase I clinical trials are designed to test the safety (non-toxicity) ofdrugs and find the maximum tolerated dose (MTD). This task becomessignificantly more challenging when multiple-drug dose-combinations (DC) areinvolved, due to the inherent conflict between the exponentially increasing DCcandidates and the limited patient budget. This paper proposes a novel Bayesiandesign, SDF-Bayes, for finding the MTD for drug combinations in the presence ofsafety constraints. Rather than the conventional principle of escalating orde-escalating the current dose of one drug (perhaps alternating between drugs),SDF-Bayes proceeds by cautious optimism: it chooses the next DC that, on thebasis of current information, is most likely to be the MTD (optimism), subjectto the constraint that it only chooses DCs that have a high probability ofbeing safe (caution). We also propose an extension, SDF-Bayes-AR, that accountsfor patient heterogeneity and enables heterogeneous patient recruitment.Extensive experiments based on both synthetic and real-world datasetsdemonstrate the advantages of SDF-Bayes over state of the art DC trial designsin terms of accuracy and safety.

SDF-Bayes: Cautious Optimism in Safe Dose-Finding Clinical Trials with Drug Combinations and Heterogeneous Patient Groups