Real-World Evidence in Drug Development and Evaluation

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AAIR
Act Score
advanced statistical modeling
ALCL
artificial intelligence drug development
Bayesian analysis
Bayesian Sensitivity Analysis
benefit risk assessment
Category=PNB
causal inference methods
Clinical Practice
clinical trials
Convolutional Layers
Deep Learning Applications
drug development
drug development and evaluation
drug discovery
electronic health records
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Fluticasone Furoate
Ich E9
IPTW Method
ISPOR Task Force
Mixture Cure Model
observational studies
oncology outcomes research
pharmaceutical innovation
pharmaceutical practitioners
pharmacoepidemiology
Population Based Cancer Registry
Pragmatic Trials
Propensity Score
Propensity Score Adjustment
Propensity Score Model
PS Method
RCT Data
real-world data
Real-world evidence
RWE
safety and efficacy assessment
Seer Data
Systemic Anaplastic Large Cell Lymphoma
TAVR
Unmeasured Confounding
Unobserved Confounder

Product details

  • ISBN 9780367637019
  • Weight: 303g
  • Dimensions: 156 x 234mm
  • Publication Date: 29 Aug 2022
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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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

Harry Yang, Ph.D., is Vice President and Head of Biometrics at Fate Therapeutics. He has 25 years of experience across all aspects of drug research and development, from early target discovery, through pre-clinical, clinical, and CMC programs to regulatory approval and post-approval lifecycle management. He has published 7 statistical books, 15 book chapters, and over 90 peer-reviewed papers on diverse scientific and statistical subjects. He is a frequent invited speaker at national and international conferences. He also developed statistical courses and conducted training at the FDA and USP.

Binbing Yu, Ph.D., is Associate Director in the Oncology Statistical Innovation group at AstraZeneca. He serves as the statistical expert across the whole spectrum of drug R&D, including drug discovery, clinical trials, operation and manufacturing, clinical pharmacology, oncology medical affairs and post-marketing surveillance. He obtained his PhD in Statistics from the George Washington University. His primary research interests are clinical trial design and analysis, cancer epidemiology, causal inference in observation studies, PKPD modeling and Bayesian analysis.