A Bayesian adaptive phase II clinical trial design accounting for spatial variation

Beibei Guo, Yong Zang

Research output: Contribution to journalArticle

1 Scopus citations


Conventional phase II clinical trials evaluate the treatment effects under the assumption of patient homogeneity. However, due to inter-patient heterogeneity, the effect of a treatment may differ remarkably among subgroups of patients. Besides patient’s individual characteristics such as age, gender, and biomarker status, a substantial amount of this heterogeneity could be due to the spatial variation across geographic regions because of unmeasured or unknown spatially varying environmental and social exposures. In this article, we propose a hierarchical Bayesian adaptive design for two-arm randomized phase II clinical trials that accounts for the spatial variation as well as patient’s individual characteristics. We treat the treatment efficacy as an ordinal outcome and quantify the desirability of each possible category of the ordinal efficacy using a utility function. A cumulative probit mixed model is used to relate efficacy to patient-specific covariates and geographic region spatial effects. Spatial dependence between regions is induced through the conditional autoregressive priors on the spatial effects. A two-stage design is proposed to adaptively assign patients to desirable treatments according to each patient’s spatial information and individual covariates and make treatment recommendations at the end of the trial based on the overall treatment effect. Simulation studies show that our proposed design has good operating characteristics and significantly outperforms an alternative phase II trial design that ignores the spatial variation.

Original languageEnglish (US)
Pages (from-to)3187-3204
Number of pages18
JournalStatistical Methods in Medical Research
Issue number10-11
StatePublished - Nov 1 2019


  • Bayesian adaptive design
  • Markov random field
  • Personalized medicine
  • conditionally autoregressive
  • phase II trial
  • spatial variation

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

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