Editors Cosmatos (early development and clinical pharmacology, Wyeth Research) and Chow (biostatistics and bioinformatics, Duke U. School of Medicine) use contributions from experts all over the world to explore the emerging field of translational medicine (TM), and how this discipline serves the flow of information between research laboratories and patient care. Written for pharmaceutical scientists, researchers and clinicians, this book describes the structure of an effective TM practice through the development of biomarkers in clinical trials, the use of genomic and clinical databases to establish treatment protocols and the application of statistical models for evaluating data. A concluding chapter discusses the implementation of clinical TM protocols in traditional Chinese medicine. Annotation ©2009 Book News, Inc., Portland, OR (booknews.com)
Examines Critical Decisions for Transitioning Lab Science to a Clinical Setting
The development of therapeutic pharmaceutical compounds is becoming more expensive, and the success rates for getting such treatments approved for marketing and to the patients is decreasing. As a result, translational medicine (TM) is becoming increasingly important in the healthcare industry – a means of maximizing the consideration and use of information collected as compounds transition from initial lab discovery, through pre-clinical testing, early clinical trials, and late confirmatory studies that lead to regulatory approval of drug release to patients.
Translational Medicine: Strategies and Statistical Methods suggests a process for transitioning from the initial lab discovery to the patient’s bedside with minimal disconnect and offers a comprehensive review of statistical design and methodology commonly employed in this bench-to-bedside research.
Documents Alternative Research Approaches for Faster and More Accurate Data Judgment Calls
Elaborating on how to introduce TM into clinical studies, this authoritative work presents a keen approach to building, executing, and validating statistical models that consider data from various phases of development. It also delineates a truly translational example to help bolster understanding of discussed concepts.
This comprehensive guide effectively demonstrates how to overcome obstacles related to successful TM practice. It contains invaluable information for pharmaceutical scientists, research executives, clinicians, and biostatisticians looking to expedite successful implementation of this important process.
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