Comparative review of novel model-assisted designs for phase I/II clinical trials

Biom J. 2024 Jun;66(4):e2300398. doi: 10.1002/bimj.202300398.

Abstract

In recent years, both model-based and model-assisted designs have emerged to efficiently determine the optimal biological dose (OBD) in phase I/II trials for immunotherapy and targeted cellular agents. Model-based designs necessitate repeated model fitting and computationally intensive posterior sampling for each dose-assignment decision, limiting their practical application in real trials. On the other hand, model-assisted designs employ simple statistical models and facilitate the precalculation of a decision table for use throughout the trial, eliminating the need for repeated model fitting. Due to their simplicity and transparency, model-assisted designs are often preferred in phase I/II trials. In this paper, we systematically evaluate and compare the operating characteristics of several recent model-assisted phase I/II designs, including TEPI, PRINTE, Joint i3+3, BOIN-ET, STEIN, uTPI, and BOIN12, in addition to the well-known model-based EffTox design, using comprehensive numerical simulations. To ensure an unbiased comparison, we generated 10,000 dosing scenarios using a random scenario generation algorithm for each predetermined OBD location. We thoroughly assess various performance metrics, such as the selection percentages, average patient allocation to OBD, and overdose percentages across the eight designs. Based on these assessments, we offer design recommendations tailored to different objectives, sample sizes, and starting dose locations.

Keywords: clinical trial design; dose finding; model‐assisted design; operating characteristics; phase I/II trials.

Publication types

  • Comparative Study
  • Research Support, N.I.H., Extramural
  • Review
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Biometry* / methods
  • Clinical Trials, Phase I as Topic* / methods
  • Clinical Trials, Phase II as Topic* / methods
  • Humans
  • Models, Statistical*
  • Research Design