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Realworld Evidence In Drug Development And Evaluation 1st Edition Harry Yang

  • SKU: BELL-43687748
Realworld Evidence In Drug Development And Evaluation 1st Edition Harry Yang
$ 31.00 $ 45.00 (-31%)

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Realworld Evidence In Drug Development And Evaluation 1st Edition Harry Yang instant download after payment.

Publisher: Chapman and Hall/CRC
File Extension: PDF
File size: 14.23 MB
Pages: 208
Author: Harry Yang, Binbing Yu
ISBN: 9780367026219, 9780367637019, 9780429398674, 036702621X, 0367637014, 0429398670
Language: English
Year: 2021
Edition: 1

Product desciption

Realworld Evidence In Drug Development And Evaluation 1st Edition Harry Yang by Harry Yang, Binbing Yu 9780367026219, 9780367637019, 9780429398674, 036702621X, 0367637014, 0429398670 instant download after payment.

Real-world evidence (RWE) has been at the forefront of pharmaceutical innovations. It plays an important role in transforming drug development from a process aimed at meeting regulatory expectations to an operating model that leverages data from disparate sources to aid business, regulatory, and healthcare decision making. Despite its many benefits, there is no single book systematically covering the latest development in the field. Written specifically for pharmaceutical practitioners, Real-World Evidence in Drug Development and Evaluation, presents a wide range of RWE applications throughout the lifecycle of drug product development. With contributions from experienced researchers in the pharmaceutical industry, the book discusses at length RWE opportunities, challenges, and solutions. Features Provides the first book and a single source of information on RWE in drug development Covers a broad array of topics on outcomes- and value-based RWE assessments Demonstrates proper Bayesian application and causal inference for real-world data (RWD) Presents real-world use cases to illustrate the use of advanced analytics and statistical methods to generate insights Offers a balanced discussion of practical RWE issues at hand and technical solutions suitable for practitioners with limited data science expertise

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