Approaches to evaluation of treatment effect in randomized clinical trials with genomic subset

Pharm Stat. 2007 Jul-Sep;6(3):227-44. doi: 10.1002/pst.300.


With the advances in human genomic/genetic studies, the clinical trial community gradually recognizes that phenotypically homogeneous patients may be heterogeneous at the genomic level. The genomic technology brings a possible avenue for developing a genomic (composite) biomarker to predict a genomically responsive patient subset that may have a (much) higher likelihood of benefiting from a treatment. Randomized controlled trial is the mainstay to provide scientifically convincing evidence of a purported effect a new treatment may demonstrate. In conventional clinical trials, the primary clinical hypothesis pertains to the therapeutic effect in all patients who are eligible for the study defined by the primary efficacy endpoint. The aspect of one-size-fits-all surrounding the conventional design has been challenged, particularly when the diseases may be heterogeneous due to observable clinical characteristics and/or unobservable underlying the genomic characteristics. Extension from the conventional single population design objective to an objective that encompasses two possible patient populations will allow more informative evaluation in the patients having different degrees of responsiveness to medication. Building in conventional clinical trials, an additional genomic objective can generate an appealing conceptual framework from the patient's perspective in addressing personalized medicine in well-controlled clinical trials. There are many perceived benefits of personalized medicine that are based on the notion of being genomically proactive in the identification of disease and prevention of disease or recurrence. In this paper, we show that an adaptive design approach can be constructed to study a clinical hypothesis of overall treatment effect and a hypothesis of treatment effect in a genomic subset more efficiently than the conventional non-adaptive approach.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Biomarkers*
  • Genomics / statistics & numerical data*
  • Humans
  • Likelihood Functions*
  • Models, Genetic*
  • Monte Carlo Method
  • Phenotype
  • Randomized Controlled Trials as Topic / methods*
  • Treatment Outcome


  • Biomarkers