Journal of the Formosan Medical Association
Volume 107, Issue 12, Supplement , Pages S61-S73, December 2008

Statistical Methods in Translational Medicine

  • Shein-Chung Chow

      Affiliations

    • Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina, USA
    • National Cheng Kung University, Tainan, Taiwan
    • Corresponding Author InformationCorrespondence to: Professor Shein-Chung Chow, Department of Biostatistics and Bioinformatics, Duke University School of Medicine, 2400 Pratt Street, Room 0311 Terrace Level, Durham, NC 27705, USA
    • ,
  • Siu-Keung Tse

      Affiliations

    • City University of Hong Kong, Hong Kong SAR, China
    • ,
  • Min Lin

      Affiliations

    • Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina, USA

Received 26 July 2008; received in revised form 23 September 2008; accepted 8 October 2008.

Article Outline

This study focuses on strategies and statistical considerations for assessment of translation in language (e.g. translation of case report forms in multinational clinical trials), information (e.g. translation of basic discoveries to the clinic) and technology (e.g. translation of Chinese diagnostic techniques to well-established clinical study endpoints) in pharmaceutical/clinical research and development. However, most of our efforts will be directed to statistical considerations for translation in information. Translational medicine has been defined as bench-to-bedside research, where a basic laboratory discovery becomes applicable to the diagnosis, treatment or prevention of a specific disease, and is brought forth by either a physician—scientist who works at the interface between the research laboratory and patient care, or by a team of basic and clinical science investigators. Statistics plays an important role in translational medicine to ensure that the translational process is accurate and reliable with certain statistical assurance. Statistical inference for the applicability of an animal model to a human model is also discussed. Strategies for selection of clinical study endpoints (e.g. absolute changes, relative changes, or responder-defined, based on either absolute or relative change) are reviewed.

Key Words:  bench-to-bedside , biomarker development , lost in translation , one-way translation , two-way translation

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PII: S0929-6646(09)60010-3

doi:10.1016/S0929-6646(09)60010-3

Journal of the Formosan Medical Association
Volume 107, Issue 12, Supplement , Pages S61-S73, December 2008

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