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DGGF 20th annual meeting PUEKS presentation

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A project with the aim to standardize an RBM approach in clinical trials. It unites four companies and academic organizations, focuses on the evaluation and optimization of Risk-based Monitoring (RbM). For this purpose, PUEKS will use data available from past clinical studies to select substantiated key risk indicators (KRIs). Subsequently, the obtained data-driven KRIs will be tested in an ongoing trial. A comparative evaluation with historical data from past studies will be additionally performed to evaluate the power of the selected KRIs in terms of cost savings, enhanced quality, and risk mitigation. The project is aimed at delivering a robust RbM tool as well as an optimized procedure for the successful implementation of RbM.

This project (HA project no. 448/14-38) is funded in the framework of Hessen ModellProjekte, financed with funds of LOEWE – Landes-Offensive zur Entwicklung Wissenschaftlich-ökonomischer Exzellenz, Förderlinie 3: KMU-Verbundvorhaben (State Offensive for the Development of Scientific and Economic Excellence).

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DGGF 20th annual meeting PUEKS presentation

  1. 1. Artem Andrianov, PhD, MBA Cyntegrity, 2015 LIFE DATA INTEGRITY The PUEKS Project – Process Optimization in Clinical Trial Monitoring Applying RbM Analytics to Improve Patient Safety and Preventive Care RbM from Idea to Implementation
  2. 2. INTRODUCTION This project (HA project no. 448/14–38) is funded in the frame- work of Hessen ModellProjekte, financed with funds of LOEWE – Landes-Offensive zur Entwicklung Wissenschaftlich-ökonomischer Exzellenz, Förderlinie 3: KMU- Verbundvorhaben (State Offensive for the Development of Sci- entific and Economic Excellence).
  3. 3. “MOTIVATION” 3 “The sponsor should develop a systematic, prioritized, risk- based approach to monitoring clinical trials. A combination of on-site and centralized monitoring activities may be appropriate. (...) Centralized monitoring is a remote evaluation of ongoing and/or cumulative data collected from all trial sites in a timely manner. Emerging advances in technology may facilitate the remote monitoring of source data when appropriate.” Source: Integrated Addendum to ICH E6 (R1): Guideline for Good Clinical Practice E6 (R2), Draft Addendum, 5.18.3.
  4. 4. RbM – Simply Explained RbM: Clinical Sites Monitor (CRA) Risk Profiles On-Site Visits Centralized Monitoring Traditional: Clinical Sites Monitor (CRA) On-Site Visits 4
  5. 5. 5 Types of Risks in Clinical Trials? Study-level Risks Site-level Risks Operational Risks Fixed Risks Dynamic Risks
  6. 6. PUEKS Project
  7. 7. Project Partners PUEKS 7
  8. 8. PUEKS Project Time Frame: Nov 2014 - May 2016 Title: Process Innovation in Clinical Trial Monitoring Goal: Development and validation of a Risk-based Monitoring process using robust data-driven risk indicators Assessment of the predictive and pre- ventive power of the risk indicators Retrospective analysis of a finished trial Implementation in an ongoing study 8
  9. 9. 9 Detection Issue Management Mitigation Process Knowledge Exchange Risk-based Management 4 PILLARS OF RbM
  10. 10. 10 Primary Outcome Measures Protocol Compliance Fraud Detection Key Risk Defense Strategy Sponsor Specific Risks Study Specific Risks
  11. 11. TECHNOLOGICAL ASPECTS
  12. 12. Essential Technology Features Clinical Risk Management Data Quality Monitoring Site Ranking • Umbrella principle – unification of CTMS, EDC, etc. • Risk mitigation communication • Concentration on systemic risk, not random artefacts • P-value control for essential variables • Geographical quality spread • Data quality dynamics and comparison values • Transparent communication and site involvement • Historical root-cause feedback graph Following the guidelines of FDA and EMA 12
  13. 13. Fraud & Misconduct Detection
  14. 14. 14 Data-ExportsCRO-sMeasurementsSites Data Defect Types “Professional patients” – people who participate in more than one trials simultaneously • Manipulation with protocol • Misuse of measurement tools • Outdated calibration • Inclusion criteria fraud • Manipulation of parameters for trial inclusion • Error-hiding • Unintended technical defects • Unnoticed data defects • Error-hiding • Inventing new data (forging) • Reporting only selected observations (cooking) • Deleting outliers (trimming)
  15. 15. Case Study #1: Inclusion Rate per Week 15 Observations: • Too many sites with few data • Trial did not reach the target of included subjects. About the trial: • Mid-sized phase III trial • Therapy area: rheumatoid arthritis • 50 Sites • Duration: Q2/2010–Q4/2014
  16. 16. Randomization Rate per Week 16 Observations: • Randomization troubles as a result of poor recruitment
  17. 17. Case Study #2: Site 1068: Variance Too Small 17 HAQ (Health assessment questionnaire) Inliers that vary not enough (potentially: artificially created) Hm… too many problems with HAQ? Randomization & Inclusion: too few patients
  18. 18. Inlier Analysis: Site 1068 Non-responder Patient 1 - Very ill Patient 2 – (healthy) Misuse of inclusion criteria? 18
  19. 19. Preference of Digits (Site 90 as example) 19 Digits Frequency
  20. 20. Unusual Date of the Week/Time (Weekend Checks) 20 Site Number Patients Visit on
  21. 21. Enrolment Control ENROLMENT Eligible Not eligible
  22. 22. Application of Poisson-gamma Model 22 Our Approach (Based on Anisimov‘s Methods): Use stochastic processes to address uncertainties in prior information and variability of enrolment and related operational processes • Patients arrive at sites according to Poisson processes • Variation in enrolment rates is modelled using a gamma distribution • Delays in site initiation can be random Traditional Approaches: Based on the analysis of mean flows, mainly deterministic setting Source: V. Anisimov, “Recruitment modeling and predicting in clinical trials,” American Pharmaceutical Outsourcing, vol. 10, no. 1, pp. 44–48, 2009.
  23. 23. Key Take-Aways
  24. 24. Key Take-Aways  Treat fixed and dynamic risks differently  RACT* can cover most of fixed risks, for dynamic risks a separate solution is needed  The main risk indicators (RIs) demand thorough analysis, verification and validation  The selection of indicators must be guided by GCP, patient safety, protocol and common sense  Only a combination of RIs can tell the full story  Do not forget about a residual risk, it demands a separate approach  Technology plays a critical role in effective RbM. However, the monitoring team is more important. 24 *RACT – Free Risk-Assessment Categorization Tool (RACT) developed by TransCelerate Group
  25. 25. Irving Fisher (1867-1947) 25 RBM

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