Data integrity can be implemented using several approaches. One of the most effective ways to implement DI is a risk based approach. The speaker elaborates this.
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Best Practices to Risk Based Data Integrity at Data Integrity Conference, London, UK
1. 1
BEST PRACTICES TO
IMPLEMENT A RISK BASED
APPROACH FOR ENSURING
DATA INTEGRITY
Dr. Bhaswat S. Chakraborty
Former Sr.VP, & Chair, R&D Core Committee, Cadila Pharma
Former Director, Biopharmaceutics, Biovail, Canada
Former Senior Reviewer, TPD, Canada
1
Presented at the 2nd Data Integrity and Protection
for Clinical Research Summit, London, UK,
December 7-8, 2017
2. 2
CONTENTS
Understanding the risk based monitoring
(RBM) for quality & data integrity
Risk identification, analysis and other
important aspects of RBM
Developing protocols & procedures to ensure
data integrity through RBM
Computer & Statistical Systems
DSMB & Trainning of sponsor/CRO
Concluding remarks 2
3. 3
Investigational
Sites
Product
Management
Project
Management
Drug & Clinical Trial Development
Extended Picture
IRB Regulatory
Documents
Relationship
Building
eMails
Partners &
Affiliates
Meetings
CROs
Contracts
Knowledge
Information
Safety
Communication
Resource
Management
Data Capture
Data Management
Multidirectional Flow of Data and Decisions
3
4. 4
IMPORTANCE OF DATA INTEGRITY
Quality of clinical trial data can make or
break an NDA or BLA – all phases
Clinical development is very complex and
highly expensive
Quality monitoring of CT data may cost up to
30% of total trial cost
Quality of trial data, whether of patient safety
or of effucacy & scientific conduct of the entire
trial is determined by accuracy, completeness
and proper documentation of all data
5. 5
CLINICAL TRIAL DATA AND DOCUMENTS
Study and site feasibility documents
Protocol
Inclusion/Exclusion criteria
Informed Consent
Investigators brochure
Training documents and data
Data
Randomisation, Blinding
CRF/ECRF (Demographics and site visit data)
Primary and secondary outcome variables (end points)
Clinical procedures and study conduct data
Investigational products: Supply, Inventory, Handling & Usage, Retention
Safety monitoring and signal detection
Subject withdrawal and retention data
Data and safety monitoring committee (activities, data, reports)
Data management and data monitoring including SDV by Sponsor/CRO
Data recording and reporting
Statistical analysis
Study reporting
..
5
6. 6
WHY DO WE NEED A DATA MANAGEMENT &
DATA INTEGRITY SYSTEM?
Enormous volumes of data
Example, a Phase-III trial in 10 centres with
100 patients each
60 pages of CRF for each recruited patient
20 fields each page
40 pages of screening form for each candidate
patient
20 fields each page
[1000 (60 x 20)] + [1500 (40 x 20)]
= 12, 00000 + 12, 00000
= 24,00000 specific data points 6
7. 7
CLINICAL TRIAL DATA
Useful only if it is clean & accurate
Data processing must be
real-time
subject randomization
management of clinical trials materials
laboratory uploads
patient diary data
Integrated
Consistent
Accurate
Data structures must be
Standard
Validated
Data transfer method must be
Standard
Validated
7
8. 8
DATA INTEGRITY IN CLINICAL RESEARCH
“Data integrity is the degree to whicha collection of
data is complete, consistent and accurate through the
data lifecycle.” – WHO
Research integrity depends on data integrity
Includes all aspects of collection, use, storage and sharing of
data.
Data integrity is a shared responsibility
Although the main responsibility belongs to the PI asnd the
sponsor, there is a broader role and responsibility for the
institute and scientific community.
Transparency of the research data is its CREDIBILTY
8
Free and accurate information exchange is
fundamental to scientific progress
Van Eyk J., JHU NHLBI Innovative Proteomics Center on Heart Failure
9. 9
SOURCES OF DATA INTEGRITY & ITS LACK
Data integrity is based on accurate and traceable:
Collection
Recording
Storage
Reporting.
Data integrity can be compromised numerous ways:
Malicious proprietors
Human mistakes and naivety
Technical error
9
Van Eyk J., JHU NHLBI Innovative Proteomics Center on Heart Failure
Fraud & cooked data are the highest risk of intefrity
but errors can also give misleading results
10. 10
TRADITIONAL MONITORING OF CT DATA
Often aimed at 100% source data verification (not required by
ICH or FDA)
Resource intensive – expensive, requires on-site visits
Can identify certain and trends
data entry errors, missing data in source records or CRFs
provide assurance that study documentation exists
assess compliance with the protocol and investigational
product
quality of the overall conduct of the trial at that site
particularly helpful early in a study, especially if protocol is
complex and includes novel procedures
lead to meaningful training efforts
Evidence exists that fraud, fabrication of data and suspicious
non-random data distribution are not picked up by traditional
monitoring
11. 11
MONITORING REQUIRED IN DIFFERENT
PHASES OF CLINICAL TRIAL
Phase I trial involves relatively high risk to a small N
Usually the study investigator performs continuous monitoring of
safety
Phase II trial follows phase I with more N
Toxicity and outcomes are confounded by disease process
Monitoring similar to that of a phase I trial or additional monitoring by
experts or DSMB
Phase III trials frequently compares a new treatment to
a standard treatment or to no treatment
Large N
Short-term risk is low, but long term effects of IP to achieve significant
safety or efficacy difference from the control
May require a DSMB to perform monitoring functions 11
12. 12
RISK BASED MONITORING (RBM) –
USFDA, EMA & ICHGCP E6R2
A centralized, risk-based
monitoring (RBM) is the new
directive/amendment in quality
monitoring of CTs
ICH GCP E6R2 directs to use
some of the best practices of RBM:
5.0.1 Identify critical trial
processes and data
5.0,2 Identify risks to critical
trial processes and data
5.0.3 Evaluate risks
5.0.4 Control risks
5.0.5 Communicate risks
5.0.6 Review risks
5.0.7 Report risks
Essentially
same
principles
of RBM
13. 13
APPROACHES TO BUILD AND MAINTAIN
DATA INTEGRITY
Monitoring, meaning RBM
Centralized monitoring with supervised & unsupervised ML
CTDM & RDC Systems
On site monitoring
Clinical trial quality assurance units (QAUs)
Sponsors often use internal or external QAUs
QbD and Risk based monitoring
Building QbD
Risk identification & assessment
Critical attributes and riskcategorization thereof
Plans and processes
Targeted monitoring
13
14. 14
Clinical Data Management System
(CDMS)
Data Capture Strategy
Remote Data Capture
Portal Data Capture
Processes
Adverse Event Monitoring System
Compliance (GCP/GLP) Monitoring
Workflow Monitoring
Analytical Data Processing
Statistical Data Processing
Systems
Data Extraction
GLIB
TMS/Dictionaries
Reports
Validation
14
15. 15
CENTRALIZED MONITORING
A remote evaluation carried out by sponsor personnel or CRO
By clinical monitors, data management personnel, or
statisticians
At a location other than the sites
Can provide many of the capabilities of on-site monitoring as
well as value additions
Success of centralized monitoring depend on various factors
Use of electronic systems; access to subjects’ electronic records
Timeliness of data entry from paper CRFs
Ensure that record keeping, data entry & supporting source
data are well-defined & accessible
Identify in monitoring plan when one or more on-site
monitoring visits are required
15Centralized monitoring plus RDC are key for Risk-
based Monitoring
USFDA Guidance (2013) on Oversight of Clinical Investigations-RBM
16. 16
ALTERNATE MONITORING
Monitor or review data quality
missing data, inconsistent data, outliers, and protocol deviations
Conduct statistical analyses to identify data trends, e.g.,
checks of range, consistency, completeness, unusual data distribution
Analyze site characteristics, performance metrics
high screen failures, withdrawal rates, high eligibility violations,
delays
Verify critical source data remotely
where accessible; CRF data are according to the protocol?
Complete administrative and regulatory tasks
IRB approvals, IP accountability, randomization and CRF data
Communication with Study Site Staff – Tele- or
videoconferencing, email
Review site’s processes, procedures, and records technique
16
Many of the above elements can be used for Risk-
based Monitoring
17. 17
RISK-BASED MONITORING (1)
Basis: Monitoring activities prevent or mitigate
important and likely sources of malpractices or
errors in conduct, collection, and reporting of critical
data and processes necessary for human subject
protection and trial integrity
Importance of Critical Quality Factors:
Procedures critical to collecting reliable data for study
endpoints
Consistency across sites or in a highly specific manner in
some sites
Procedures that won’t significantly impact data analysis or
subject safety
17
Other than deliberate malpractices, some types of errors
in CT is more important than others
(error in age v/s error in endpoint)
USFDA Guidance (2013) on Oversight of Clinical Investigations-RBM
18. 18
RISK-BASED MONITORING (2)
RBM relies on a systematic process of identification,
asses, control, share and review the risks (CT data,
event & procedures during CT’s entire lifecycle)
Determination of when should a site(s) get extensive
intervention or review?
Include supervised and unsupervised central approaches
Supervised RBM is data- and trial specific based on established
risk-indicators and thresholds
Unsupervised statistical monitoring is holistic & free from
fixed hypotheses; uses statistical tests to ensure data quality &
integrity
18
RBM for Data integrity includes Centralized & On-site
monitoring plus some machine learning
USFDA Guidance (2013) on Oversight of Clinical Investigations-RBM
19. 19
RISK-BASED MONITORING (3)
1. Identify Critical Data and Processes to be Monitored:
IC verification, adherence to protocol eligibility criteria,
accountability and administration of IP, conduct,
documentation & assessments related to study endpoints & red
safety assessments
Procedures essential to trial integrity, e.g., blinding is
maintained, both at the site level and at the sponsor level
2. Risk Assessment:
Risk identification based on trial design or investigational
product
Risks assessed and prioritized by likelihood of errors occurring,
impact of such errors on subject protection and trial integrity
19
Some types of errors in CT is more important than others
(error in age v/s error in endpoint)
USFDA Guidance (2013) on Oversight of Clinical Investigations-RBM
20. 20
Risk-based Monitoring (4)
3. Factors to consider while developing a monitoring plan:
Complexity of the study design may require increased frequency and
extent of review (adaptive designs, stratified designs, complex dose
titrations..)
4. Monitoring Plan:
Each monitoring method & how it will be used to address
Criteria for determining the timing, frequency, and extent of planned
monitoring activities
5. Documentation of monitoring:
Date of the activity and the individual(s) conducting and participating
in it
Summary of the data or activities reviewed
Description of noncompliances, potential noncompliance, data
irregularities..
A description of any actions taken
20Use the results of risk assessment in developing
monitoring plan and type and intensity of monitoring to
address this risksUSFDA Guidance (2013) on Oversight of Clinical Investigations-RBM
21. 6. Risk control & communication:
Risk control aims at determination of an acceptable risk level
Reduces excessive risks to an acceptable level
Risk control includes risk mitigations, adaptations & risk
acceptance actions
Also includes accountability for risk control
Risk communication ensures that risk assessment and
mitigation activities (including updates) are communicated to all
relevant personnel
7. Risk review and reporting
In risk prone trials, many new information come from parallel
activities and tests (preclinical, pharmacology, IB, protocol
amendments)
Thus regular review of previous and new data should be done,
reported & necessary actions taken
21
Risk-based Monitoring (5)
Expert group on CT (2017) on implementation on regulation (EU) No 536/2014
22. 22
Computer Systems and Non-compliance
ICHGCP R2 5.5.3a & 5.5.3h:
When using a computerzed system, base the
validation approach on a risk assessment,
maintain SOPs & ensure data integrity
ICHGCP R2 5.20.1
Follow up of non-compliance that has or may
signicicantly affect human subject protection or
reliability of trial results, by performing a root
cause analysis & 9mplementing CAPA
ICH GCP R2, Step-4
23. 23
ELEMENTS OF MACHINE LEARNING
Machine
Learning
Unsupervised Supervised
Cluster & interpret
data based only on
input data
Supervised
Develop predictive
models based on
input & outpuy data
24. 24
Supervised Modeling & Unsupervised
Statistics
Idea of supervised modeling is data specific (both
input & output data)
Risk indicators & their thresholds
Important risk indicators are built in RDC
Risk predictions (above thresholds) are based on expert
models & acted upon for mitigation
Unsupervised statistical RBM are based on actual
trial data
Can identify out of trend or non-random values, e.g. sites
recruiting very low or very high; site showing too many
ADRs
Univariate & multivariate analysis
Chakraborty B (2017) unpublished results; Bengtsson S. (2017), Lund University
26. 26
Require a DSMB to Oversee Data
Integrity?
CTs that are complex and are not of low risk
(refer EMA directive on Risks Proportionate
Approaches in CTs) usually need a DSBM to
maintain data & trial integrity
To ensure that participants are not exposed to undue
risks
To ensure that the study will yield unbiased & usable
results
To do Interim Analyses and/or change protocol study
design based on IA
To deliberate on malpractice & serious errors
26
Low risk studies, e.g. Phase-I, bioavailability, very short
term studies do not require DSMB
27. Registration of studies on http://www.clinicaltrial.gov/
Selection and monitoring of clinical investigators
Selection of monitors
Monitoring procedures and activities
Safety/ AE reporting
All study tabulations
All investigators tabulations
Data tabulations on each subject in each CT in an NDA
eRecords and eSignatures
Data collection
System & data handling during site closure
27
Preparing as a Sponsor or a CRO for an
FDA Audit
Pro active preparation for Regulatory audit often is half the
battle won for data integrity demonstration
Various USFDA & EMA guidelines
28. 28
CONCLUDING REMARKS
A CT is as good as the quality of its data (i.e. Data of integrity)
In an effort to ensure the integrity of CT data, the FDA, EMA &
ICH have released requirements
Monitoring of data collection, review and analysis is essential to
ensure data integrity
Even traditional monitoring requires an in-depth and comprehensive
examination of all collected data, but fails to identify data integrity risks
The risk-based monitoring (RBM) is fundamentally different as
to how data managers review clinical data
Does not mandate a specific methodology but requires an ideal strategy
allowing a faster time to market, reduces site monitoring costs and frees up
time and resources for value-added tasks
For complex Phase III (sometimes Phase II) trials require a
DSMB for ensuring data integrity or to stop the trial
Training and audit (FDA/Client) readiness for data integrity
assures high success rates
28