  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.
