This article presents a practical application of a new dose-finding algorithm with a continual reassessment method (CRM) to a phase I study identifying the recommended dose level of chemotherapy in patients with advanced breast cancer. First, we conducted a preliminary study to determine a prior distribution for a model parameter to be used in the CRM, based on prestudy perceptions of a panel of oncologists. During the phase I study, dose-limiting toxicity (DLT) data were continually monitored to guide decisions on dose escalation and de-escalation, based on a CRM using a Bayesian probability model. We analyzed 16 patients, 3 of whom experienced a DLT. The dose-toxicity curve was updated based on observed toxicity data. The recommended doses were very similar to those identified in a previous dose-escalation study using a conventional 3 + 3 cohort design. The main disadvantage of the CRM was the need for dedicated statistical support, but this was outweighed by more accurate estimation of recommended doses.
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