Optimal Enrollment Timing in Early-Stage Patient-Centered Clinical Trials

Problem definition: Early-stage patient-centered clinical trials aim to establish the toxicity profile of investigational drugs and determine safe dose ranges for future trials using cohort-specific consent. Patients considering participation face intricate benefit-risk tradeoffs, particularly regarding the timing of their enrollment: Early enrollment corresponds to low doses with potentially low efficacy and toxicity with high uncertainty around them, whereas late enrollment corresponds to high doses with potentially high efficacy and toxicity with reduced uncertainty, but also an increased risk of disease progression and trial termination. Methodology/results: This paper develops a Bayesian model where a patient determines the optimal enrollment time based on health status and evolving beliefs about dose toxicity and efficacy. The patient periodically learns about the benefits and risks of doses and enrolls in the trial at the optimal time to maximize the expected benefits. We show that the optimal policy is of a control-limit type, where the threshold is determined by when the benefit of taking the current dose exceeds the benefit of waiting for future doses. We also establish sufficient conditions for the existence of a control-limit enrollment time policy based on the patient’s health status and beliefs. Managerial implications: Our study offers valuable insights into the nuanced benefit-risk tradeoffs patients face in enrolling early-stage clinical trials, especially the dynamic interplay between their deteriorating health status and evolving beliefs about the drug’s efficacy-toxicity profile. The control-limit structure of the optimal policy is robust across most settings; for example, the structure is preserved under response delays from previous trials, under other trial designs such as the continual reassessment method, and when trial exclusion criteria are imposed. Our case study using lung cancer clinical trial data further illustrates how factors like belief and observation uncertainties influence patient decision-making. Funding: This work was supported by the National Science Foundation, the Division of Civil, Mechanical, and Manufacturing Innovation [2308750], Air Force Office of Scientific Research [FA9550-24-1-0241]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2025.0430 .