Statistical methods in translational medicine.
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 physicianscientist 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.
Chow
Shein-Chung
SC
Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina, USA.
Tse
Siu-Keung
SK
Lin
Min
M
D000818:Animals; D015415:Biomarkers / Q000032:analysis*; D035843:Biomedical Research / Q000639:trends*; D002986:Clinical Trials as Topic; D004341:Drug Evaluation; D023381:Endpoint Determination / Q000706:statistics & numerical data; D006801:Humans; D015233:Models, Statistical; D012107:Research Design; D018401:Sample Size
D015415:Biomarkers