cdm processes c1
TRANSCRIPT
Overall CDM Process
ICRI
Introduction
CDM is consistently being recognized as a primary part of clinical development team & in some instances leads this team!
Evolution
CDM has evolved from a data entry process into a diverse process
“to provide clean data in a useable format in a timely manner”
“provide a database fit for use” “ensuring data are clean & database is ready to lock” Now CDM manages
entry of CRF data merging of non-CRF data systems & processes designed to identify bad data generate & track CRFs & queries determine protocol violators interact with site personnel to resolve data issues
CDM as a Science Factors contributing to CDM as a subject
New technologies Growth predictions Globalization Need for a supporting infrastructure
Role of CDM in overall drug development organization is continuing to evolve
Relationships with other organizations are continuing to be defined & developed
CDM is a very visible & strong organization now Considered as an integral, respected, highly valued
member of clinical development team
Importance of CDM
CDM is a vital vehicle in Clinical Trials to ensure integrity & quality of data being transferred from trial subjects to a database system
To provide consistent, accurate, & valid clinical data
To support accuracy of final conclusions & report
GCP Guidelines All clinical research data should be recorded, handled, & stored in a way
that allows its accurate reporting, interpretation & verification. (ICH GCP
2.10, 4.9, 5.5, 5.14 & ICH E9 3.6 & 5.8)
Systems with procedures that assure quality of every aspect of research
should be implemented. (GCP 2.13)
Quality assurance & quality control systems with written standard
operating procedures should be implemented & maintained to ensure that
research are conducted & data are generated, documented & recorded,
& reported in compliance with protocol, GCP & applicable regulatory
requirements. (GCP 5.1.1)
GCP Guidelines
If data are transformed during processing, it should always be possible to compare original data & observations with processed data (ICH GCP 5.5.4)
Sponsor should use an unambiguous subject identification number or code that allows identification of all data reported for each subject. (ICH GCP 5.5.5)
Protocol amendments that necessitate a change in design of CRF, subject diaries, study worksheets, research database & other key aspects of CDM processes need to be controlled. (ICH E9 2.1.2)
Common standards should be adopted for a number of features of research such as dictionaries of medical terms, definition & timing of main measurements, handling of protocol deviations. (ICH E9 2.1.1)
Clinical Data Management
CDM refers to management of data capture & data flow processes in conduct of a clinical research
It begins with design of data capture instrument & data collection, continues with data QC procedures to assure quality of all aspects of process, & ends with database finalization
Objectives of CDM
To ensure: That collected data is complete & accurate so
that results are correct That trial database is complete & accurate, & a
true representation of what took place in trial That trial database is sufficiently clean to
support statistical analysis, & its subsequent presentation & interpretation
Clinical Development Process
Data Collectionand Management
RegulatorySubmission
TrialManagement
SiteManagement
SiteSystems
FinancialManagement
ClinicalProgram
Management
Pharmaco-vigilance
Source:Bio-IT 2004
Clinical Development Process
ProtocolAuthoring
LabLoad
DataProcessing
Coding
DocMgmt
RegPlanning &Tracking
Stats &ReportingeSubmit
ScheduleMgmt
ProcessMetrics
Monitoring ResourceMgmt
RegulatoryReporting
MedicalInformation
SiteMgmt
Site & DrugLogistics
SitePlanning
ContractMgmt
FinancialMgmt
Drug& Vendor
Mgmt
TrialSimulation
SiteSelection
PortfolioMgmt
TrialBenchmark
Site & LabComm’s
PatientScheduling
PatientRecruitment
SitePayments& Reports
InvestigatorPayments
SiteRecruitment
SafteyMgmt Coding
Source:Bio-IT 2004
Multidisciplinary Team
1. Clinical Investigator
2. Site coordinator
3. Pharmacologist
4. Trialist/Methodologist
5. Biostatistician
6. Lab Coordinator
7. Reference lab
8. Project manager
9. Clinical Research Manager/Associate
10. Monitor
11. Regulatory affairs
12.12. Clinical Data Clinical Data ManagementManagement
13. Clinical Safety Surveillance Associate (SSA)
14. IT
15. IT/IS personnel
16. Trial pharmacist
17. Clinical supply
18. Auditor/Compliance
CDM Process
Investigator Monitor
CentralLaboratory
Data Manager
Statistician
Clinician
RegulatoryAuthority
Subject
CRF
DCF
CRF DCFSample
LabResults
ClinicalData
NDA
21 Jan 2006
PROGRAMMING
Role of DM in Clinical Research
DATAMANAGEMENT
BIOSTATISTICS
Set up database, with built in rangechecks for validating
data at the data entry stage
Create data entry forms (linked to database) with formats similar to those of CRF pages
Program complex data edits separately
Log in CRFs received via Courier or CRF images received via telephone line
Data Entry & Validation using built-in range checks on anongoing basis
Periodic Conversion of database for applying data edit checks
Run edits on converted data
Review edit lists & send queries to Sites after manual review
Interim Data Quality Audits
Query Resolution & Database Correction
Final Data Quality
Audit plus statistical quality control procedures
Database Lock
Data Flow Chart
Paper based data collection
Clinical Data CRF
Clean data
sample ortest data
analysis results
Patientenrolled in trial
Research Coordinatorcompletes paper CRF
CRF/core lab data mailed to
Data Entry Group
Data EntryData entered into
database
Data Entry QADatabase verified
to CRFs
Data queries issuedand resolved
BiostatisticianData Analysis
Monitor / CRASource Document
Verification
iterative queryresolution process
(paper faxed ormailed)
Core Lab orEvents Committeeperforms analysis
Clinical TrialsDatabase
Electronic data collection
Clinical TrialsDatabase
Patientenrolled in trial
Research Coordinator completes electronic CRF
Clinical Data
BiostatisticianData Analysis
Clean data
MonitorSource Document
Verification
CRF submittedelectronically
CRAReal-time review
of data
Electronicquery resolution
Core Lab orEvent Committeeperform analysis
sample ortest data analysis results
Acquisition or Collection of Clinical Trial Data
Data Capture Instrument CRF Design
Paper forms (‘No Carbon Required’ :NCR) Remote Data Entry Electronic data transmission from Central lab Central web based system & Other technologies
Data Source & Data Definition Identify Data Source
Study Sites Reference Lab ECG/RDE
Data Definition Identify data required (data items, study variables) Define variables Source Data Verification (SDV) Edit Checks
Validation Processes Testing of Screen vs DB structure Validation of
Range Date Format Coding field discrepancies
Testing of second entry verification file comparison batch verification
Design specifications of software Criteria for acceptance or rejection of software Results documentation Review & approval documents
Data Validation/Edit Check
Consist of computer checks on data to assure validity & accuracy of data
Validate data against predetermined specifications Primarily used to check efficacy data unique to current
study
Validation Checks
Consistency checks To highlight area where data in database are
inconsistent
Presence checks To ensure completeness of data
Range checks To identify inaccurate or invalid data & statistical outliers To ensure that data outside of permitted range are to be
clarified & verified
CRF Design Design CRF along with protocol to assure collection
of only data protocol specifies Guidelines to collect data through independent
means Design CRF with primary safety & efficacy
endpoints in mind as main goal of data collection Establish & maintain a library of standard forms CRF to be available for review at clinical site prior to
approval Use NCR paper or other means to assure exact
replicas of paper collection tools
CRF Development & Tracking CRF completion guideline is printed as parts of CRF Training sessions are conducted for investigators & SC
during study initiation meeting Receipt & Tracking of CRF Tracking process encompass verification of arrival date & its
acknowledgement & its progress through process
CRF Scanning
CRF are scanned soon after receipt in CRF Tracking System & archived electronically as backup into an image database by designated CDC
Benefits:-Effortless access to CRF images It provides a single source for most up to date copy of
CRF Ensures that original entries were not overwritten during
clinical CRF / data review
EDC Processes
Develop e-CRFs along with Monitoring, Statistics, Regulatory affairs, & Medical teams
Ensure Collection of safety data User-friendly screens Flexibility of data entry Validation procedures Query management tools Audit trails Data transfers Integration of laboratory & other non-CFR data
Data Storage Backup copies to be taken frequently Paper documents should be scanned & electronically archived Database design specifications Raw data Audit trail Original study documents Procedural Variation Documentation Database Closure Site copies of data Final data - ASCII, SAS Transport, pdf, CDISC ODM Model
External Data
Vendor-specific training Vendor Audit Data clarification process Utilize standards such as HL7, CDISC Data editing & verification procedures File formats Data transmission Database updates Data storage & archiving
Coding
Auto-encoder Dictionaries Process for change in dictionary or version Same version to be used for combined
studies Training Process for submitting changes to
dictionaries
Data Dictionaries
MedDRA An International Conference on Harmonization (ICH)
initiative, is a standardized dictionary of medical terminology WHO: WHOART, drugs
World Health Organization Adverse Reaction Terminology ICD
International Classification of Diseases FDA COSTART
Coding Symbols for a Thesaurus of Adverse Reaction Terms
Data Cleaning
Purpose, characteristics & complexity of study Critical variables
primary & secondary safety & efficacy subject identifiers
Documentation of Procedures Guidelines working practices references
Testing the process
Data Cleaning
CRF completion/data entry instructions Timelines for
data entry running data checks replicating data.
Database quality criteria Quality control plan
Image Review (a pre-entry review)
CRF image also known as working copy CRF is reviewed for accuracy, completeness & consistency of data
Any queries or discrepancy identified during Image Review were annotated
Look for problems with legibility, incorrectly completed fields, missing data & scientifically invalid or obviously inconsistent data
Data Review
Clinical data review by designated medical reviewer
Ensure complex medical data are reviewed & assessed to detect any clinical nuances in data
Data Query
A query is raised when a discrepancy or an inconsistency is noted or annotated during image review & during computer edit-check.
Subsequent changes in data must be supported by signed Data Clarification Form (DCF) or authorized Data Handling Convention
Declaring Clean File
Clean File for final database is declared when all clean data have been transferred
After declaring Clean File, editing on database will only be allowed with proper documentation
Data Closure
All data have been received & processed All queries have been resolved External data are reconciled SAEs are reconciled Coding list review Review for logic & consistency Final review Quality audit of data Error rate Updating documents
Data Closure
Blind Data Review After clean file is declared, blind data review prior to final
analysis
Data Listing Generate hardcopy listing of data for clinical study report
Data Transfer Transfer of data to another site Sponsor, Statistician, Regulatory, eg eSubmission
Electronic data archive
ArchivingElectronic repository of Clinical data Metadata Administrative data Reference data CRF or eCRF images in PDF form Program files Validation records Regulatory documents Audit trail Data structures Edit checks Transfer specifications
QA Compliance of procedures to
Regulations Written procedures
Error rates for variables used in primary & secondary safety & efficacy
Monitor aggregate data Site audits Inspections (CRF-to-database) Data quality impact analysis Quality Policy Standardized or validated data collection & handling processes Error prevention Process monitoring
QC
System Validation
By Clinical IT
SDV by CRA
ImageReview
Double Data Entry
Data Coding & Data Review
Edit Checks
CRF to Database Inspection
The Quality of overall data is thus increased because sooner a data capture problem is detected &
corrected, higher quality of final data