The entire population is firstly screened for biomarker status and all individuals enter the trial.
Alternative names: Mixture designs, Combination of trial designs, hybrid biomarker designs
Hybrid designs can be used when there is compelling prior evidence which shows detrimental effect of the experimental treatment for a specific biomarker-defined subgroup (i.e., biomarker-negative subgroup) or some indication of its possible excessive toxicity in that subgroup, thus making it unethical to randomize the patients within this population to the experimental treatment.
- Similar to the enrichment designs, hybrid designs are powered to identify treatment effect only in the biomarker-defined subgroup which is randomly assigned to the experimental or control treatment groups.
- These designs are a combination of an enrichment design where we randomize patients to either the experimental or the control treatment group and single-arm designs in biomarker-negative patients.
Sample size Formula
The same formula used for the required number of patients or events for the enrichment designs can be used for hybrid designs.
- The feasibility of a prognostic biomarker can be tested.
- Allow for better risk assessment and improved individualized treatment since it assigns patients to treatments based on risk assessment scores instead of their biomarker status (biomarker-positive and biomarker-negative patients).
- None found
- George, S.L. Statistical issues in translational cancer research. Clin. Cancer Res. 2008, 14, 5954–5958. [Google Scholar] [CrossRef] [PubMed]
- Chen, J.J.; Lu, T.-P.; Chen, D.-T.; Wang, S.-J. Biomarker adaptive designs in clinical trials. Transl. Cancer Res. 2014, 3, 279–292. [Google Scholar]
- Gosho, M.; Nagashima, K.; Sato, Y. Study designs and statistical analyses for biomarker research. Sensors 2012, 12, 8966–8986. [Google Scholar] [CrossRef] [PubMed]
- Buyse, M.; Sargent, D.J.; Grothey, A.; Matheson, A.; de Gramont, A. Biomarkers and surrogate end points—The challenge of statistical validation. Nat. Rev. Clin. Oncol. 2010, 7, 309–317. [Google Scholar] [CrossRef] [PubMed]
- Mandrekar, S.J.; Sargent, D.J. Clinical trial designs for predictive biomarker validation: Theoretical considerations and practical challenges. J. Clin. Oncol. 2009, 27, 4027–4034. [Google Scholar] [CrossRef] [PubMed]
- Mandrekar, S.J.; Sargent, D.J. Clinical trial designs for predictive biomarker validation: One size does not fit all. J. Biopharm. Stat. 2009, 19, 530–542. [Google Scholar] [CrossRef] [PubMed]
- Tajik, P.; Zwinderman, A.H.; Mol, B.W.; Bossuyt, P.M. Trial designs for personalizing cancer care: A systematic review and classification. Clin. Cancer Res. 2013, 19, 4578–4588. [Google Scholar] [CrossRef] [PubMed]
- Van Schaeybroeck, S.; Allen, W.L.; Turkington, R.C.; Johnston, P.G. Implementing prognostic and predictive biomarkers in CRC clinical trials. Nat. Rev. Clin. Oncol. 2011, 8, 222–232. [Google Scholar] [CrossRef] [PubMed]
- Sparano, J.A.; Paik, S. Development of the 21-gene assay and its application in clinical practice and clinical trials. J. Clin. Oncol. 2008, 26, 721–728. [Google Scholar] [CrossRef] [PubMed]
- Sato, Y.; Laird, N.M.; Yoshida, T. Biostatistic tools in pharmacogenomics—Advances, challenges, potential. Curr. Pharm. Des. 2010, 16, 2232–2240. [Google Scholar] [CrossRef] [PubMed]
- Collette, L.; Bogaerts, J.; Suciu, S.; Fortpied, C.; Gorlia, T.; Coens, C.; Mauer, M.; Hasan, B.; Collette, S.; Ouali, M.; et al. Statistical methodology for personalized medicine: New developments at EORTC headquarters since the turn of the 21st century. Eur. J. Cancer Suppl. 2012, 10, 13. [Google Scholar] [CrossRef]
- Lin, J.-A.; He, P. Reinventing clinical trials: A review of innovative biomarker trial designs in cancer therapies. Br. Med. Bull. 2015, 114, 17–27. [Google Scholar] [CrossRef] [PubMed]
- European Medicines Agency. Reflection Paper on Methodological Issues Associated with Pharmacogenomic Biomarkers in Relation to Clinical Development and Patient Selection. Available online: http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2011/07/WC500108672.pdf (accessed on 10 October 2015).
- Wang, S.-J.; O’Neill, R.T.; Hung, H.M.J. Approaches to evaluation of treatment effect in randomized clinical trials with genomic subgroup. Pharm. Stat. 2007, 6, 227–244. [Google Scholar] [CrossRef] [PubMed]