Interface between Randomized Controlled Trials and Real-World Evidence Studies

Name: Interface between Randomized Controlled Trials and Real-World Evidence Studies
Brand: Not Stated
SKU: 9781394369652
Price: 496.74 PLN
Availability: InStock

Autor: Jie Chen, Naitee Ting

Język:

Oprawa: Miękka

Wydawca: Not Stated

Dostępność: Dostępna u dostawcy w małych ilościach

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496.74 zł

Integrate RCTs and real-world evidence for stronger regulatory submissions Since the passage of the...

Informacje o książce

Autor

Jie Chen, Naitee Ting

Język

Angielski

Oprawa

Książka - Miękka

Data wydania

2026

strony

480

EAN

9781394369652

Enbook ID

48382199

Wydawca

Not Stated

Waga

666

Pełny opis

Integrate RCTs and real-world evidence for stronger regulatory submissions Since the passage of the 21st Century Cures Act, the landscape of clinical research has transformed dramatically. Today, the integration of real-world data (RWD) and real-world evidence (RWE) with traditional randomized controlled trials (RCTs) is not just innovative - it is essential for modern drug development and regulatory decision-making. This comprehensive volume bridges the methodologies of conventional RCTs and emerging RWE studies, offering systematic approaches for leveraging diverse data sources throughout the clinical development lifecycle. Grounded in regulatory statistics yet forward-looking in scope, the book explores Stein's continuum of study designs - from traditional RCTs to pragmatic trials and observational studies - and demonstrates how RWD can enhance trial feasibility, inform outcome selection, provide external controls for single-arm trials, and strengthen post-approval safety monitoring. Readers will find detailed explorations of: A history of clinical trials, evidence-based medicine, and the regulatory frameworks governing drug approvalTreatment switching and estimands in RWE studiesAdvanced analytical considerations including precision medicine, vaccine effectiveness, safety evaluation, and sensitivity analysisPractical roadmaps for designing robust RWE studies Artificial intelligence and machine learning in clinical research Designed as both a reference and a graduate-level textbook, this book serves clinical researchers such as clinicians and biostatisticians, regulatory professionals, public health scientists, and industry practitioners seeking to harness the power of RWD and RWE to generate credible, actionable evidence for modern medicine.

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Interface between Randomized Controlled Trials and Real-World Evidence Studies

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