Contextual Constrained Learning for Dose-Finding Clinical Trials
This addresses challenges in medical clinical trials for researchers and practitioners by providing a constrained learning approach, though it appears incremental as it builds on existing contextual and constrained methods.
The paper tackles the problem of dose-finding clinical trials with limited budgets and heterogeneous patient populations by proposing C3T-Budget, an algorithm that maximizes drug efficacy while learning about the drug under safety constraints, as demonstrated in a simulated study with efficient budget usage and balanced trade-offs.
Clinical trials in the medical domain are constrained by budgets. The number of patients that can be recruited is therefore limited. When a patient population is heterogeneous, this creates difficulties in learning subgroup specific responses to a particular drug and especially for a variety of dosages. In addition, patient recruitment can be difficult by the fact that clinical trials do not aim to provide a benefit to any given patient in the trial. In this paper, we propose C3T-Budget, a contextual constrained clinical trial algorithm for dose-finding under both budget and safety constraints. The algorithm aims to maximize drug efficacy within the clinical trial while also learning about the drug being tested. C3T-Budget recruits patients with consideration of the remaining budget, the remaining time, and the characteristics of each group, such as the population distribution, estimated expected efficacy, and estimation credibility. In addition, the algorithm aims to avoid unsafe dosages. These characteristics are further illustrated in a simulated clinical trial study, which corroborates the theoretical analysis and demonstrates an efficient budget usage as well as a balanced learning-treatment trade-off.