Clinical data Management process by devi007

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									Overall CDM Process
ICRI

Introduction
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CDM is consistently being recognized as a primary part of clinical development team & in some instances leads this team!

Evolution
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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
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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
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Factors contributing to CDM as a subject
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New technologies Growth predictions Globalization Need for a supporting infrastructure

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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
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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)
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Systems with procedures that assure quality of every aspect of research should be implemented. (GCP 2.13)

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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
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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
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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 Collection and Management Site Management Regulatory Submission
Site Systems Pharmacovigilance

Financial Management

Trial Management Clinical Program Management

Source:Bio-IT 2004

Clinical Development Process
Data Processing Protocol Coding Authoring Lab Load

Financial Mgmt Drug Investigator & Vendor Payments Mgmt Contract Mgmt
Schedule Mgmt Resource Monitoring Mgmt Process Metrics

Site Planning Site Site Recruitment Mgmt Site & Drug Logistics

Reg Planning & Tracking Stats & eSubmit Reporting Doc Mgmt
Site Payments & Reports Patient Site & Lab Recruitment Comm’s Patient Scheduling Regulatory Reporting Saftey Coding Mgmt Medical Information

Site Selection Trial Portfolio Benchmark Mgmt Trial Simulation

Source:Bio-IT 2004

Multidisciplinary Team
1. 2. 3. 4. 5. 6. 7. 8. 9. Clinical Investigator Site coordinator Pharmacologist Trialist/Methodologist Biostatistician Lab Coordinator Reference lab Project manager Clinical Research Manager/Associate 10. Monitor 11. Regulatory affairs 12. Clinical Data Management 13. Clinical Safety Surveillance Associate (SSA) 14. IT 15. IT/IS personnel 16. Trial pharmacist 17. Clinical supply 18. Auditor/Compliance

CDM Process
Subject

CRF DCF Investigator Sample Monitor CRF DCF Clinical Data Regulatory Authority Clinician
21 Jan 2006

Statistician NDA

Lab Results

Central Laboratory

Data Manager

Role of DM in Clinical Research
DATA MANAGEMENT

PROGRAMMING

BIOSTATISTICS

Data Flow Chart
Set up database, with built in range checks 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 an ongoing basis

Periodic Conversion of database for applying data edit checks

Final Data Quality Audit plus statistical quality control procedures

Interim Data Quality Audits

Run edits on converted data

Database Lock

Query Resolution & Database Correction

Review edit lists & send queries to Sites after manual review

Paper based data collection
sample or test data analysis results

Core Lab or Events Committee performs analysis Clinical Data CRF

Patient enrolled in trial

Research completes

Coordinator paper CRF

Monitor / CRA Source Document Verification

CRF/core lab data mailed to Data Entry Group Clinical Trials Database

iterative query resolution process (paper faxed or mailed)

Data queries issued and resolved

Data Entry QA Database verified to CRFs

Clean

data

Data Entry Data entered into database

Biostatistician Data Analysis

Electronic data collection
sample or test data Core Lab or Event Committee perform analysis Clinical Data analysis results

Patient enrolled in trial

Research Coordinator completes electronic CRF

CRF submitted electronically

Electronic query resolution

Monitor Source Document Verification

CRA Real-time review of data

Clean data Clinical Trials Database

Biostatistician Data Analysis

Acquisition or Collection of Clinical Trial Data
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Data Capture Instrument
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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
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Identify Data Source
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Study Sites Reference Lab ECG/RDE
Identify data required (data items, study variables) Define variables Source Data Verification (SDV) Edit Checks

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Data Definition
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Validation Processes
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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

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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
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Range checks
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To identify inaccurate or invalid data & statistical outliers To ensure that data outside of permitted range are to be clarified & verified

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Consistency checks
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To highlight area where data in database are inconsistent
To ensure completeness of data

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Presence checks
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CRF Design
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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
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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
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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
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Develop e-CRFs along with Monitoring, Statistics, Regulatory affairs, & Medical teams Ensure
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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
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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
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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
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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
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MedDRA
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An International Conference on Harmonization (ICH) initiative, is a standardized dictionary of medical terminology World Health Organization Adverse Reaction Terminology
International Classification of Diseases Coding Symbols for a Thesaurus of Adverse Reaction Terms

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WHO: WHOART, drugs
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ICD
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FDA COSTART
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Data Cleaning
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Purpose, characteristics & complexity of study Critical variables
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primary & secondary safety & efficacy subject identifiers

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Documentation of
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Procedures Guidelines working practices references

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Testing the process

Data Cleaning
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CRF completion/data entry instructions Timelines for
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data entry running data checks replicating data.

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Database quality criteria Quality control plan

Image Review

(a pre-entry review)

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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

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Clinical data review by designated medical reviewer Ensure complex medical data are reviewed & assessed to detect any clinical nuances in data

Data Query
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A query is raised when a discrepancy or an inconsistency is noted or annotated during image review & during computer editcheck. Subsequent changes in data must be supported by signed Data Clarification Form (DCF) or authorized Data Handling Convention

Declaring Clean File
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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
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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
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Blind Data Review
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After clean file is declared, blind data review prior to final analysis
Generate hardcopy listing of data for clinical study report Transfer of data to another site Sponsor, Statistician, Regulatory, eg eSubmission

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Data Listing
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Data Transfer
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Electronic data archive

Archiving
Electronic 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
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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
Edit Data Coding Checks Double & Data Data Review Image Entry System SDV by Review Validation CRA By Clinical IT 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


								
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