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AbstractThis article considers the concept of designing Phase I clinical trials using both clinician- and patient-reported outcomes to adaptively allocate study participants to tolerable doses and determine the maximum tolerated dose (MTD) at the study conclusion. We describe a new isotonic patient-reported outcome (PRO-ISO) Phase I design with the flexibility of allocating patients to lower, more tolerable regimens if a large number of PRO-DLT events are seen at higher doses. We conduct simulation studies of the operating characteristics of the design and compared them to the patient-reported outcomes continual reassessment method (PRO-CRM). We illustrate that the PRO-ISO makes appropriate dose assignments during the study to give investigators and reviewers an idea of how the method behaves. In simulation studies, the PRO-ISO demonstrates comparable performance to the PRO-CRM in terms of recommending target doses and allocating patients to these doses. It also performs well relative to a nonparametric optimal benchmark applied to the PRO setting. Finally, we extend our methodology to account for the problem of late-onset toxicities. [ABSTRACT FROM AUTHOR]

This article addresses the problem of identifying the maximum tolerated dose (MTD) in Phase I dose-finding clinical trials with late-onset toxicities. The main design challenge is how best to adaptively allocate study participants to tolerable doses when the evaluation window for the toxicity endpoint is long relative to the accrual rate of new participants. We propose a new design framework based on order-restricted statistical inference that addresses this challenge in sequential dose assignments. We illustrate the proposed method on real data from a Phase I trial of bortezomib in lymphoma patients and apply it to a Phase I trial of radiotherapy in prostate cancer patients. We conduct extensive simulation studies to compare our design's operating characteristics to existing published methods. Overall, our proposed design demonstrates good performance relative to existing methods in allocating participants at and around the MTD during the study and accurately recommending the MTD at the study conclusion. [ABSTRACT FROM AUTHOR]