CDM Processes C1

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