BILITE: A Bayesian randomized phase II design for immunotherapy by jointly modeling the longitudinal immune response and time-to-event efficacy

Beibei Guo, Yong Zang

Research output: Contribution to journalArticle

Abstract

Immunotherapy—treatments that target a patient's immune system—has attracted considerable attention in cancer research. Its recent success has led to generation of novel immunotherapeutic agents that need to be evaluated in clinical trials. Two unique features of immunotherapy are the immune response and the fact that some patients may achieve long-term durable response. In this article, we propose a two-arm Bayesian adaptive randomized phase II clinical trial design for immunotherapy that jointly models the longitudinal immune response and time-to-event efficacy (BILITE), with a fraction of patients assumed to be cured by the treatment. The longitudinal immune response is modeled using hierarchical nonlinear mixed-effects models with possibly different trajectory patterns for the cured and susceptible groups. Conditional on the immune response trajectory, the time-to-event efficacy data for patients in the susceptible group is modeled via a time-dependent Cox-type regression model. We quantify the desirability of the treatment using a utility function and propose a two-stage design to adaptively randomize patients to treatments and make treatment recommendations at the end of the trial. Simulation studies show that compared with a conventional design that ignores the immune response, BILITE yields superior operating characteristics in terms of the ability to identify promising agents and terminate the trial early for futility.

Original languageEnglish (US)
JournalStatistics in Medicine
DOIs
StateAccepted/In press - 2020

Keywords

  • Bayesian adaptive design
  • cure models
  • immunotherapy
  • longitudinal immune response

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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