1. Risk-Based Monitoring
The time is now!
Anne S Lindblad, PhD
President and CEO, The Emmes Corporation
Rockville, Maryland, USA

2. Clinical Research Monitoring

3. Clinical Research Monitoring
Why do we monitor?
HSP and safety
Data fit for use
What do we know?
Human review limitations-humans looking for errors can miss 15% at first try
Tantsyura V, Dunn IM, Fendt K, Kim YJ, Waters J, Mitchel J. Risk-based Monitoring: A closer Statistical look at source document verification, Queries, study size effects, and data quality. Therapeutic Innovation and Regulatory Science, 2015: 1-8

4. 2011-2014 Effect of Errors Mitchel et al: Before and after monitoring
Identical means
Reduction in standard deviation (1% increase in sample size)
Smith et al:
Discrepancies equally distributed across treatment groups and sites
Lindblad et al
Found errors not picked up by extensive monitoring

5. 2011-2014 Effect of Errors Bakobaki et al
Centralized monitoring (more than standard edit checks) could identify 95% of findings from on-site
Grieve et al
Pooled error rate 2-4%
Tantsyura et al
.1%-1.4% key data points modified as a result of SDV
An average of <8% SDV is sufficient

6. Summary
TEMPER
More RBM driven visits found 1 or more major/critical finding compared to non triggered visit when reconsent removed (86% vs 60%).
Data query resolution time strongest predictor for issues followed by protocol deviations
Increasing involvement by the study coordinator reduced major/critical findings; opposite true for physicians

7. Summary
OPTIMON
Based on follow-up audit after monitoring complete, RBM approach found not non-inferior
Power lower than designed.
Primary outcome based on Investigator appraisal highest risk for non-conformity
High rate of non-conformities

8. Summary
ADAMON
Based on follow-up audit after monitoring complete, RBM approach found non-inferior to extensive on-site
Tertiary analyses: requires less than 50% of extensive on- site monitoring resources.
Study complexity increases errors
Quality by design may have mitigated some of these risks

9. ISO 31000:2009 Avoid risk Accept or increase risk Remove risk source
Change risk likelihood
Change consequences
Share risk with others

10. ISO 31000:2009 Create a risk management policy (RBM plan)
Risk criteria
Evaluation metrics
Ownership
Continuous improvement

11. Lessons Learned Quality by Design! THEN Make a Plan
Include Relevant Stakeholders DM
Statisticians
Safety team PI
CRAs
Sponsor

12. How to make a RBM Plan Identify potential source of error
Random-Training, Sloppiness
Systematic-Bias, Fraud
Incorporate Knowledge
Weight Importance According to Risk and Source
Variable, depending on study and timing

13. How to make a RBM Plan Quantify Risks Human Subjects Protection Safety
Data Fit for Use

14. Components of a Plan On-Site Monitoring Central Monitoring
Chapter heading
Components of a Plan
Informed Consent
Protocol Compliance
Pharmacy
Training/Personnel
On-Site Monitoring
Enrollment
Dashboarding accumulating data
Monitoring KRI
Algorithms
Central Monitoring
Source Data Verification
Source Data Review
AE and PD supportive material
Essential Documents
Off-Site Monitoring

15. How to make a RBM Plan Identify Responsibilities CRAs DM Statisticians
Safety team
Sponsor

16. RBM What do we gain? Enhanced error detection Early error detection
Future error mitigation
Potential cost savings (a function of study size.)

17. References
Tantsyura V, Dunn IM, Fendt K, Kim YJ, Waters J, Mitchel J. Risk-based Monitoring: A closer Statistical look at source document verification, Queries, study size effects, and data quality. Therapeutic Innovation and Regulatory Science, 2015: 1-8.
Mitchel J, Kim Y, Choi J et al. Evaluation of data entry errors and data changes to an electronic data capture clinical trial database. Drug Information Journal. 2011; 45(4):
Smith CT, Stocken DD, Dunn J et al. The vale of source data verification in a cancer clinical trial. PLoS ONE. 2012; 7(12):e51623.

18. References
Lindblad AS, Manukyan Z, Purohit-Sheth T et al. Central site monitoring: Results from a test of accuracy in identifying trials and sites failing Food and Drug Administration inspection. J Cli Trials. 2014; 11(2):
Bakabaki JM, Rauschenberger M, Nicola J et al. The potential for central monitoring techniques to replace on- site monitoring: findings from and international mulit- centre clinical trial. J Clin Trials. 2012;9(2):
Grieve AP. Source data verification by statistical sampling: issues in implementation. Drug Infrmation Journal. 2012; 46(3):

19. Questions?