A Bayesian adaptive marker-stratified design for molecularly targeted agents with customized hierarchical modeling

Yong Zang, Beibei Guo, Yan Han, Sha Cao, Chi Zhang

Research output: Contribution to journalArticle

Abstract

It is well known that the treatment effect of a molecularly targeted agent (MTA) may vary dramatically, depending on each patient's biomarker profile. Therefore, for a clinical trial evaluating MTA, it is more reasonable to evaluate its treatment effect within different marker subgroups rather than evaluating the average treatment effect for the overall population. The marker-stratified design (MSD) provides a useful tool to evaluate the subgroup treatment effects of MTAs. Under the Bayesian framework, the beta-binomial model is conventionally used under the MSD to estimate the response rate and test the hypothesis. However, this conventional model ignores the fact that the biomarker used in the MSD is, in general, predictive only for the MTA. The response rates for the standard treatment can be approximately consistent across different subgroups stratified by the biomarker. In this paper, we proposed a Bayesian hierarchical model incorporating this biomarker information into consideration. The proposed model uses a hierarchical prior to borrow strength across different subgroups of patients receiving the standard treatment and, therefore, improve the efficiency of the design. Prior informativeness is determined by solving a “customized” equation reflecting the physician's professional opinion. We developed a Bayesian adaptive design based on the proposed hierarchical model to guide the treatment allocation and test the subgroup treatment effect as well as the predictive marker effect. Simulation studies and a real trial application demonstrate that the proposed design yields desirable operating characteristics and outperforms the existing designs.

Original languageEnglish (US)
JournalStatistics in Medicine
DOIs
StatePublished - Jan 1 2019

Fingerprint

Hierarchical Modeling
Biomarkers
Treatment Effects
Subgroup
Therapeutics
Hierarchical Prior
Beta-binomial Model
Bayesian Design
Average Treatment Effect
Pemetrexed
Bayesian Hierarchical Model
Adaptive Design
Evaluate
Operating Characteristics
Hierarchical Model
Clinical Trials
Design
Statistical Models
Simulation Study
Vary

Keywords

  • adaptive design
  • Bayesian method
  • biomarker
  • clinical trial
  • hierarchical modeling
  • marker-stratified design
  • molecularly targeted agents
  • subgroup treatment effect

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

Cite this

A Bayesian adaptive marker-stratified design for molecularly targeted agents with customized hierarchical modeling. / Zang, Yong; Guo, Beibei; Han, Yan; Cao, Sha; Zhang, Chi.

In: Statistics in Medicine, 01.01.2019.

Research output: Contribution to journalArticle

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