Real-world evidence in drug development and evaluation
Author(s)
Bibliographic Information
Real-world evidence in drug development and evaluation
（Chapman & Hall/CRC biostatistics series）
CRC Press, c2021
- : hbk
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Note
Includes bibliographical references and index
Description and Table of Contents
Description
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
Table of Contents
1 Using Real-world Evidence to Transform Drug Development: Opportunities and Challenges. 2. Evidence derived from real world data: utility, constraints and cautions. 3. Real-World Evidence from Population-Based Cancer Registry Data. 4. External Control using RWE and Historical Data in Clinical Development. 5. Bayesian method for assessing drug safety using real-world evidence. 6. Real-World Evidence for Coverage and Payment Decisions. 7. Causal Inference for Observational Studies/Real-World Data. 8. Introduction to Artificial Intelligence and Deep Learning with a Case Study in Analyzing Electronic Health Records for Drug Development.
by "Nielsen BookData"