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

Clinical Research Monitoring

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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): 421-430. 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.

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):205-217. 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):257-264. Grieve AP. Source data verification by statistical sampling: issues in implementation. Drug Infrmation Journal. 2012; 46(3):368-377.

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