Re-Engineering Clinical Trials: Best Practices for Streamlining the Development Process
Editat de Peter Schueler, Brendan Buckleyen Limba Engleză Hardback – 19 dec 2014
- Highlights the latest paradigm-shifts and innovation advances in clinical research
- Offers easy-to-find best practice sections, lists of current literature and resources for further reading and useful solutions to day-to-day problems in current drug development
- Discusses important topics such as safety profiling, data mining, site monitoring, change management, increasing development costs, key performance indicators and much more
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Specificații
ISBN-13: 9780124202467
ISBN-10: 0124202462
Pagini: 360
Ilustrații: 25 illustrations
Dimensiuni: 152 x 229 x 30 mm
Greutate: 0.7 kg
Editura: ELSEVIER SCIENCE
ISBN-10: 0124202462
Pagini: 360
Ilustrații: 25 illustrations
Dimensiuni: 152 x 229 x 30 mm
Greutate: 0.7 kg
Editura: ELSEVIER SCIENCE
Public țintă
Pharmacologists, physicians and clinical researchers involved in the process of clinical development and clinical trial designCuprins
Section 1: Why Does the Industry Need a Change?
1. Why is our industry struggling?
2. What are the current main obstacles to reach drug approval?
3. Japan: An opportunity to learn?
4. The "Clinical Trial App"
Section 2: What Does Our Industry and What Do Others Do
5. What does "re-engineering" mean in our industry?
6. How can the Innovative Medicines Initiative help to make drug development more efficient?
7. Experiences with Lean and Shopfloor Management in R&D in other branches
8. Well-known methodologies, but not in our world: FMEA
Section 3: Where to Start: The Protocol
9. No patients, no data: Patient recruitment in the 21st century
10. The impact of bad protocols
11. Data mining for better protocols
12. It is all in the literature
13. What makes a good protocol better?
14. The Clinical Trial Site
Section 4: Alternative Study Designs
15. Do we need new endpoints? Surrogate and bio-marker
16. On the measurement of the disease status in clinical trials: lessons from MS
17. Generating evidence from historical data using “robust prognostic matching: Experience from Multiple Sclerosis
18. Studies with fewer patients involved: the Adaptive Trial
19. Studies with less site involvement: the Hyper Trial
20. Studies without sites: the Virtual Trial
Section 5: From Data to Decisions
21. Data standards against data overload
22. Data management 2.0
23. What do Sites Want?
24. From data to information and decision: ICONIK
25. Knowledge Management
26. Taking Control of Ever Increasing Volumes of Unstructured Data
27. Share the Knowledge based on quality data
Section 6: You Need Processes, Systems and People
28. It's all about the people (and their competencies)
29. Manage the Change
30. How Key Performance Indicators help to manage the change
Conclusions
1. Why is our industry struggling?
2. What are the current main obstacles to reach drug approval?
3. Japan: An opportunity to learn?
4. The "Clinical Trial App"
Section 2: What Does Our Industry and What Do Others Do
5. What does "re-engineering" mean in our industry?
6. How can the Innovative Medicines Initiative help to make drug development more efficient?
7. Experiences with Lean and Shopfloor Management in R&D in other branches
8. Well-known methodologies, but not in our world: FMEA
Section 3: Where to Start: The Protocol
9. No patients, no data: Patient recruitment in the 21st century
10. The impact of bad protocols
11. Data mining for better protocols
12. It is all in the literature
13. What makes a good protocol better?
14. The Clinical Trial Site
Section 4: Alternative Study Designs
15. Do we need new endpoints? Surrogate and bio-marker
16. On the measurement of the disease status in clinical trials: lessons from MS
17. Generating evidence from historical data using “robust prognostic matching: Experience from Multiple Sclerosis
18. Studies with fewer patients involved: the Adaptive Trial
19. Studies with less site involvement: the Hyper Trial
20. Studies without sites: the Virtual Trial
Section 5: From Data to Decisions
21. Data standards against data overload
22. Data management 2.0
23. What do Sites Want?
24. From data to information and decision: ICONIK
25. Knowledge Management
26. Taking Control of Ever Increasing Volumes of Unstructured Data
27. Share the Knowledge based on quality data
Section 6: You Need Processes, Systems and People
28. It's all about the people (and their competencies)
29. Manage the Change
30. How Key Performance Indicators help to manage the change
Conclusions
Recenzii
"...a good overview of problems facing the pharmaceutical industry in the design and conduct of clinical trials, especially within the current regulatory framework. Score: 74 - 3 Stars" --Doody's