Bayesian interval-based oncology dose-finding design with repeated quasi-continuous toxicity model.

In oncology dose-finding clinical trials, the key to accurately estimating the maximum tolerated dose (MTD) is to use all data efficiently given small sample sizes. Currently, popular designs dichotomize adverse events of various types and grades that occur within the first treatment cycle into binary toxicity outcomes of dose-limiting toxicity (DLT) events. Such compression of toxicity data from multiple treatment cycles causes huge loss of information, often resulting in MTD estimation with large bias and variance. To improve this, a continuous endpoint (the total toxicity profile, TTP) was proposed to incorporate adverse event types and grades. The Bayesian Repeated Measures Design (RMD) was further developed by Yin et al. (2017) to account for the cumulative toxicity information from multiple treatment cycles. However, the existing RMD method selects the dose that minimizes the loss function based on point estimates, which may generate inconsistent results due to small sample sizes in phase I trials. To reduce the variability in dose escalation decision-making, we propose an improved repeated measures design with an interval-based decision rule that selects the dose with the highest posterior probability of falling in a pre-specified target toxicity interval. Through comprehensive simulations, we compared this proposed design with the existing RMD design, along with well-established DLT-based designs such as Continual Reassessment Method (CRM) and Bayesian Logistic Regression Model (BLRM). The results demonstrated that our proposed design outperforms all other designs in terms of accurately identifying the MTD and assigning fewer patients to sub-therapeutic or overly toxic doses.