Bio

Dr. Dehua (Edward) Bi is a biostatistician specializing in Bayesian parametric and nonparametric methods for clinical trial design, survival analysis, and machine-learning applications. He is currently a postdoctoral fellow at the Stanford Cancer Institute, jointly mentored by Dr. Ying Lu and Dr. Crystal Mackall. Dr. Bi earned his PhD from The University of Chicago under the mentorship of Dr. Yuan Ji. His research focused on developing novel Bayesian methods for information borrowing, Bayesian transfer learning, digital twins, designing early-phase clinical trials, and creating flexible sample-size estimation and adaptation approaches under the Bayesian framework.

Professional Education

  • Doctor of Philosophy, University of Chicago (2024)
  • PhD, The University of Chicago, Biostatistics (2024)
  • MS, Boston University, Systems Engineering (2018)
  • B.A.S, University of Waterloo, Mechatronics Engineering (2015)

Stanford Advisors

Contact

  • Academic
    dehuabi@stanford.edu
    University - Scholar Department: Stanford Cancer Institute Core Position: Postdoctoral Scholar

Additional Info

All Publications

  • Balancing the effective sample size in prior across different doses in the curve-free Bayesian decision-theoretic design for dose-finding trials. Journal of biopharmaceutical statistics Xu, J., Bi, D., Fan, S. K., Lee, B. L., Lu, Y. 2026: 1-19

    Abstract

    The primary goal of dose allocation in phase I trials is to minimize patient exposure to subtherapeutic or excessively toxic doses, while accurately recommending a phase II dose that is as close as possible to the maximum tolerated dose (MTD). Fan introduced a curve-free Bayesian decision-theoretic design (CFBD), which leverages the assumption of a monotonic dose-toxicity relationship without directly modeling dose-toxicity curves. This approach has also been extended to drug combinations for determining the MTD. Although CFBD has demonstrated improved trial efficiency by using fewer patients while maintaining high accuracy in identifying the MTD, it may artificially inflate the effective sample sizes for the updated prior distributions, particularly at the lowest and highest dose levels. This can lead to either overshooting or undershooting the target dose. In this paper, we propose a modification to CFBD's prior distribution updates that balances effective sample sizes across different doses. Simulation results show that with the modified prior specification, CFBD achieves a more focused-dose allocation at the MTD and offers more precise-dose recommendations with fewer patients on average. It also demonstrates robustness to other well-known dose finding designs in literature.

    View details for DOI 10.1080/10543406.2026.2627388

    View details for PubMedID 41733250

https://orcid.org/0009-0000-1959-1347