Bayesian adaptive dose-finding studies with delayed responses

In recent years, Bayesian response-adaptive designs have been used to improve the efficiency of learning in dose-finding studies. Many current methods for analyzing the data at the time of the interim analysis only use the data from patients who have completed the study. Therefore, data collected at intermediate time points are not used for decision making in these studies. However, in some disease areas such as diabetes and obesity, patients may need to be studied for several weeks or months for a drug effect to emerge. Additionally, slow enrollment rates can limit the number of patients who complete the study in a given period of time. Consequently, at the time of an interim analysis, there may be only a small proportion (e.g., 20%) of patients who have completed the study. In this paper, we propose a new Bayesian prediction model to incorporate all the data (from patients who have completed the study and those who have not completed) to make decisions about the study at the interim analysis. Examples of decisions made at the interim analysis include adaptive treatment allocation, dropping nonefficacious dose arms, stopping the study for positive efficacy, and stopping the study for futility. The model is able to handle incomplete longitudinal data including missing data considered missing at random (MAR). A utility-function-based decision rule is also discussed. The benefit of our new method is demonstrated through trial simulations. Three scenarios are examined, and the simulation results demonstrate that this new method outperforms traditional design with the same sample size in each of these scenarios.