Journal of the Formosan Medical Association
Volume 107, Issue 12, Supplement , Pages S19-S27, December 2008

Utility of Adaptive Strategy and Adaptive Design for Biomarker-facilitated Patient Selection in Pharmacogenomic or Pharmacogenetic Clinical Development Program

  • Sue-Jane Wang

      Affiliations

    • Corresponding Author InformationCorrespondence to: Dr Sue-Jane Wang, Office of Biostatistics, Office of Translational Sciences, Center for Drug Evaluation and Research, US FDA, HFD-700, WO 21, Mail Stop Room 3562, 10903 New Hampshire Avenue, Silver Spring, MD 20993, USA

Office of Biostatistics, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Maryland, USA

Received 31 August 2008; received in revised form 9 September 2008; accepted 2 October 2008.

Article Outline

In the early to late phases of conventional clinical trials, improvement of disease status at study baseline is the anchor of an effective treatment measured by therapeutic response. These population-based clinical trials do not formally account for disease-associated marker genotype or genome-associated therapeutic response. We discuss alternative study designs in pharmacogenomic or pharmacogenetic clinical trials for genomic or genetic biomarker development, and for formally assessing the clinical utility of genomic or genetic (composite) biomarkers. A two-stage adaptive strategy from completed, ongoing or prospectively planned pharmacogenomic or pharmacogenetic clinical trials is described for development of a genomic or genetic biomarker. We present two types of adaptive design: (1) the genomic biomarker is developed external to the clinical trial, which is designed for treatment effect inference; and (2) first-stage data are used to explore a genomic biomarker, but statistical inference of treatment effect in the genomically or genetically defined biomarker subset is only performed at the second stage of the same trial. When the null hypothesis of no treatment effect in all randomized patients and the genomic patient subset are prospectively specified, we compare the statistical power between fixed and adaptive designs. We also compare the two types of adaptive design. Results from simulation studies showed that adaptive design is more powerful than fixed design for those genomic or genetic biomarkers whose clinical utility is predictive of treatment effect. Pursuit of adaptive design gains at least 20% to more than 30% genomic patient subset power when the genomic biomarker status is readily usable at study initiation, in comparison to when it is explored using the first-stage data of the same clinical trial. In exploratory studies, adaptive strategy provides wide flexibility in the process of genomic or genetic biomarker development. In contrast, an adaptive design trial that employs limited flexibility, and is an adequate and well-controlled investigation, has a greater power gain than a fixed design trial, in which the genomic biomarker is capable of predicting treatment effects that pertain only to the prespecified genomic or genetic patient subset.

Key Words:  genomic biomarker , patient adaptation , personalized medicine , prospective/retrospective study , study design , treatment effect

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PII: S0929-6646(09)60005-X

doi:10.1016/S0929-6646(09)60005-X

Journal of the Formosan Medical Association
Volume 107, Issue 12, Supplement , Pages S19-S27, December 2008

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