Risk-group-specific dose finding based on an average toxicity score

Abstract

We propose a Bayesian dose-finding design that accounts for two important factors, the severity of toxicity and heterogeneity in patients' susceptibility to toxicity. We consider toxicity outcomes with various levels of severity and define appropriate scores for these severity levels. We then use a multinomial-likelihood function and a Dirichlet prior to model the probabilities of these toxicity scores at each dose, and characterize the overall toxicity using an average toxicity score (ATS) parameter. To address the issue of heterogeneity in patients' susceptibility to toxicity, we categorize patients into different risk groups based on their susceptibility. A Bayesian isotonic transformation is applied to induce an order-restricted posterior inference on the ATS. We demonstrate the performance of the proposed dose-finding design using simulations based on a clinical trial in multiple myeloma.

Figure 1

Scenarios in the simulation study for the multiple myeoloma trial. The horizontal and vertical axes are dose and average toxicity score, respectively. The ATS ≡ 0.25 is represented by the horizontal dotted line. True ATS’s for the low risk group (40 < GFR ≤ 60 mL/min), moderate risk group (20 < GFR ≤ 40 mL/min), and high risk group (GFR ≤ 20 mL/min) are represented by a circle, triangle, and square, respectively. Table 1: Simulation results using the proposed design for dose finding with risk groups. For each scenario we provide the true ATS by risk group (italicized numbers in table) and report the correct selection probabilities and the average number of patients per dose for each risk group.