Preface
Part I. Real-World Data and Evidence to Accelerate Medical Product Development
The need for real world data/evidence in clinical development and life cycle management, and future directions
Overview of current RWE/RWD landscape
Key considerations in forming research questions
Part II. Fit-for-use RWD Assessment and Data Standards
Assessment of fit-for-use real-world data sources and applications
Key variables ascertainment and validation in real-world setting
Data standards and platform interoperability
Privacy-preserving data linkage for real-world datasets
Part III. Causal Inference Framework and Methodologies in RWE Research
Causal Inference with Targeted Learning for Producing and Evaluating Real-World Evidence
Framework and Examples of Estimands in Real-World Studies
Clinical Studies Leveraging Real-World Data Using Propensity Score-Based Methods
Recent statistical development for comparative effectiveness research beyond propensity-score methods
Innovative Hybrid Designs and Analytical Approaches leveraging Real-Word Data and Clinical Trial data
Statistical challenges for causal inference using time-to-event real-world data
Sensitivity Analyses for Unmeasured Confounding: This is the way
Sensitivity analysis in the analysis of real-world data
Personalized medicine with advanced analytics
Use of Real-World Evidence in Health Technology Assessment Submissions
Part IV. Application and Case studies
Examples of applying causal-inference roadmap to real-world studies
Applications using real-world evidence to accelerate medical product development
The use of real-world data to support the assessment of the benefit and risk of a medicine to treat spinal muscular atrophy
Index.