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By: (Edited by) Dennis Cosmatos , (Edited by) Shein-Chung Chow
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Translational medicine has become increasingly important in pharmaceutical research development. Translational Medicine: Strategies and Statistical Methods examines the critical decisions that must be made to successfully transition from basic to clinical science. With a review of existing processes, the book presents a detailed discussion of alternative research approaches that lead to faster and more accurate decisions on data. It provides insight on biomarker development in early phase clinical trials, integrates genomic and clinical databases for the establishment of medical predictive models in various diseases, and reviews the design and analysis for translating research findings.
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 patients 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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