Autor: Schueler, Peter
Wydawca: Elsevier
Dostępność: 3-6 tygodni
Cena: 460,95 zł
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ISBN13: |
9780128244906 |
Autor: |
Schueler, Peter |
Oprawa: |
Paperback |
Rok Wydania: |
2020-11-06 |
Tematy: |
MBGR1 |
Innovation in Clinical Trial Methodologies: Lessons Learned during the Corona Pandemic presents a selection of updated chapters from Re-Engineering Clinical Trials that feature innovative options and methods in clinical trials. The Coronavirus pandemic is an accelerator for digitalization in many industries, including clinical trials. This book considers best practices, alternative study concepts requiring fewer patients, studies with less patient interaction, the design of "virtualized" protocols, and moving from data to decisions. This book will be helpful to pharmacologists, physicians and clinical researchers involved in the process of clinical development and clinical trial design.
Considers multiple digital and virtual strategiesExplores best practices, including the use of reduced patient involvementBrings together expert, trusted information to increase the efficiency and effectiveness of clinical trials
Section 1 Introduction 1. Is the Covid pandemic an accelerator for digitalization? 2. Best practices for streamlined development
Section 2 Alternative study concepts requiring fewer patients 3. Use of historic data 4. Adaptive and platform trials 5. A vision: Studies without patients
Section 3 Studies with less patient interaction 6. Home Nursing replacing site visits 7. Telemedicine replacing site visits
Section 4 Design of "virtualized" protocols 8. Data mining for better protocols 9. Patient-centric registries for population enrichment 10. Make a protocol "patient centric" 11. The use of new digital endpoints 12. Regulations for apps in clinical trials 13. The role of the site and the patient 14. A real virtual trial
Section 5 From data to decisions 15. Data standards against data overload 16. Data analytics for operational excellence 17. Taking control of high data volume 18. Share the (digital) knowledge based on quality data
Section 6 Conclusions
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