Handling Third Party Vendor Data_Katalyst HLS

1. Handling Third Party Vendor Data
Presenter: Priya Gehlawat

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3.  Often during the conduct of a clinical trial, external data which is not
included in CRFs will be collected. If not included in the primary safety
or efficacy parameters, these data can be used for subject screening,
routine safety and quality-of-life monitoring, or trend analysis.
 To speed up the process and minimize the use of different analyzing
methodologies and equipments, it is common for sponsors to refer to
the use of centralized vendors.
 The vendors provide electronic transfer of computerized data into the
sponsor’s database, thereby offering quick results, standardized testing,
and reference and calibration values applied to data collected across
study sites with the potential to eliminate transcription errors and key
entry of data.
Overview
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4. After completing this chapter you will be able to understand:
– The different kinds of Third Party Data in clinical trials
– The need and significance of Laboratory data generated in clinical trial
– The common problems and challenges faced in handling lab data
– PK/Biomarker data
– The reconciliation process of the Third Party Vendor (TPV) data
Objectives
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5. • Third Party Data also called as non- CRF/eCRF data
• TPV data is transferred from external sources into the sponsor
database via secure server for further processing
• TPV data may or may not be part of your clinical database
• Lab data constitutes as one of the major component of the TPV
data
• Hospitals and physicians office laboratories perform 75% of all
analyses. The remaining 25% are performed by independent
reference laboratories
• The international standard in use today for the accreditation of
medical laboratories is ISO 15189 - Medical laboratories - particular
requirements for quality and competence.
Do You Know
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6. Types of External Data
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External data can originate from different sources, but it is a
common practice for a centralized vendor to specialize and
produce one or more major data types. Examples of data types
include:
 Safety Laboratory
 PK/PD Data (may or may not be provided by the central lab)
 PGX (Pharmacogenetics)
 Biomarkers
 Device Data (ECG, Flowmetry, Vital Signs, Images, and other)
 Electronic Patient Diaries
 IVRS data (which is majorly used for subject randomization).

7.  TPV data reports highlight the inconsistencies between the Third
Party Data (Lab, PK, etc.) and the data captured on the clinical
database. It can be automated where in queries will be generated
within a system.
 Using Key Variables, these data are reconciled
 Key variables are those data that uniquely describe each sample
record. Key Variables are of 2 types: Primary and Secondary.
 Without such variables, it is difficult (if not impossible) to match
patient, sample, and visit with the results records accurately
 Completeness in the choice of variables collected and transferred
offers a way to increase the accuracy and overall quality of the
process.
TPV Discrepancy Reports
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8. TPV Discrepancy Reports
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Primary Key Variables
•Sponsor Name/ID
•Study/Protocol ID
•Site/Investigator ID
•Subject Identifier (Subject
Number, Screening Number or
number assigned by the CRF
used)
•Clinical Event ID (Visit Number)
•Sample ID or Sample number
•Accession number
Secondary Key Variables
•Subject’s Gender
•Subject’s Date of Birth
•Subject’s Initials
•Transmission Data/Time
•Date associated with the Subject
Visit
•Sequence Number (when more
than one observation per record
exists)
TPV data reports have "certain primary and secondary key
variables" which will be considered while reconciling the reports

9. Laboratory data is an integral part of CDM Process as it is used to:
 Screen patients to be included or excluded in a trial
 Test the efficacy of the investigational drug
 Ensure the continued safety of a patient after administration of new treatment
 Can be used as an indicator for systemic toxicities
Laboratory data in a clinical trial may be received typically in 2 ways :
• Local Lab data
 Sample analyses done in local labs
 Data collected in CRF
• Central Lab data
 Lab analyses done in a central lab
 Data not collected in CRF gets externally loaded into sponsor database
Significance and Types of Laboratory Data
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10. Lab Data Flow for Local Lab
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If data is entered into CRF then
data loading step is not
required.

11. Lab Data Flow for Central Lab

12.  In some studies the laboratory assessments for subjects at each visit
may be performed in a Local laboratory as per study requirements or
as per protocol
 Local labs which are general on-site in the hospital, or medical unit
where the patient visit is taking place
 The lab samples may be collected at site and sent collectively to the
local lab or the subjects may be directed to a given lab for lab
assessments
 The original copy of the lab reports are sent by the local lab to the site
 The investigator refers to the lab reports and then enters the results on
the CRF manually
Local Lab Data
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13.  In most of the studies, the laboratory assessments for subjects at
each visit may be performed by the contracted Central
laboratory as per the agreement or study requirements.
 Examples of Central Lab Vendors are Covance, CCLS, Quintiles
etc.
 The lab samples may be collected from site and collectively
batch shipped to the Central lab
 TPD Loader will load the data into the Clinical Database for further
analysis
Central Lab Data
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14. Local v/s Central Laboratory
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Local Laboratory
•Expertise in a Specialized Test
•Faster Analysis
•Quicker Screening of patients
•Tests as per requirement
•Low Cost
•Avoids Transportation Problems
•Examples: Labs at the site -
Hospital
Central Laboratory
•To accelerate Process
•Use of Standard
•Analyzing Methodologies across
sites
•Equipment
•Electronic Transfer of data
•Quick Results
•Standardized Testing
•Standardized Reference &
Calibration Values
•Eliminates Transcription Errors
•Examples: Quintiles, Covance,
Metropolis, etc

15.  Before initiating production transfers, test data transfer is requested
from the Third party vendor to validate the execution of the
loading programs.
 Upon successful loading of TPD data, Data Manager will start
validation and reconciliation of the data.
 Data Managers will raise queries to the site or the central lab
accordingly using the data validation reports or system
generated discrepancies
• Example:
1. Sample collection Date for Visit 3 is 15 Jan 2012 on the CRF,
however, no sample collection date on the lab Report .
Work around possibility - Query site for clarification/ confirmation
on sample
collection date as it is missing in lab data.
Lab Data Reconciliation
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16. • Data Validation Reports:
Reports to populate the discrepancies/inconsistencies between Vendor
and Clinical database e.g. SAS reports, Discrepancy listings etc.
• System Generated Queries:
Programmed checks in the system which fires on the
discrepancies/inconsistencies
between Vendor and Clinical database
• Manual Queries:
Queries raised manually in the system based on the manual review
performed by
the data management , clinical team or the discrepancy populated
from data
validation reports
Lab Data Reconciliation
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17. 17
Query Process Flow
Lab Data Reconciliation

18. List of documents that are maintained/required during reconciliation:
 Vendor Issue Log
 Missing sample tracker
 Vendor agreement for data transfer
 Edit Check Specification document
 Study Protocol
 Data Handling Plan
 e-CRF completion guidelines
 Any other project or study specific documents
Lab Data Reconciliation
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19. Examples (contd.)
• Visit 3 data entered at wrong visit in Vendor database (e.g. Visit 3
date is 3-Dec-2013 in e-CRF, but 3-Dec-2013 visit date is tagged
under Visit 4 at vendor Database)- Query Site for confirmation
• Date of Birth mismatch- Query Site for confirmation
• Sample collection date mismatch- Query Site for confirmation
Lab Data Reconciliation
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20.  Bio analytical samples are analyzed to determine
pharmacokinetic (PK) concentration of study molecule in a
biological samples for a particular study.
 Bio analytical samples may be analyzed by sponsor in-house labs
or may be analyzed by the Third Party Vendor (TPV).
 On receiving PK data, DM team will start reconciliation of the
data using validation reports or system generated queries.
Pharmacokinetic Data
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21. Reconciliation Example:
 Sample Taken is ‘Yes’ at Clinical database, Sample Received is ‘No’
at Vendor Database-Query Site for confirmation
 Sample Taken is ‘Yes’ at Clinical Database, Sample details missing at
vendor database -Query Site for confirmation
 Sample Logged in PK vendor database, but missing in Clinical
Database - Query Site for confirmation
 Incomplete Sample Status at PK Bio analytical database- Query
Vendor
PK Reconciliation
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22.  Biomarker (is defined as a characteristic that is objectively
measured and evaluated as an indicator of normal biological
processes, pathogenic processes, or pharmacologic responses to
a therapeutic intervention) sample may be analyzed by local lab
or central lab.
 If the biomarker data is analyzed by the local lab, then the data
will be entered into the clinical database (collected on CRF) in
most of the cases.
 If the biomarker data is analyzed by the central labTPV, TPD
Loader will load the TPV data into the Clinical database.
Reconciliation:
 For each study, at the time of each transfer, DM will reconcile
clinical and vendor database using validation reports or system
generated discrepancies
 DM will raise queries to the siteTPV accordingly
Biomarker Data and Reconciliation
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23.  ECG data will either be analyzed by TPV or local labs
 ECG data will either be collected on CRF or handled externally or
loaded into the study database
 TPD Loader will load the TPV data into the Clinical database.
Reconciliation:
 Reconciliation reports will be generated or automated
discrepancies will fire in the system
 DM will raise queries to the site/TPV accordingly
ECG Data
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24.  IVRS system provides the real time clinical trials data tracking for
- Patient Randomization
- Drug Dispensing
 If the data is analyzed by the TPV, Data Loader will upload this data
in clinical database for further reconciliation
 Queries will be raised to site/TPV as per discrepancies fired in
discrepancy reports or automated queries in system
IVRS (Interactive Voice Response System) Reconciliation
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25. Examples
 IVRS date mismatch (Vendor and Clinical Database)
 Patient status mismatch (Vendor and Clinical Database)
 Inconsistency in Actual Medication Pack number and Assigned
Medication Pack Number
IVRS (Interactive Voice Response System) Reconciliation
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26. Common issue with the data reports:
 Excel Limitation -Excel 2007 can hold approx. 1050000 records so if
there are more records it will not be captured
Resolution:
 Use of SAS programs/Coma Separated Value (CSV) for
reconciliation of large amount of data
 Focus should be on quality of data so time required for
reconciliation or processing of data should be agreed upon with
the relevant teams
General Understanding
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27. • Proposing project plan for handling third party deliverables
• Ensures that the deliverables are cascaded, reviewed and
finalized on time by DM Team
• Ensures that phases, system design, development, validation and
Go-Live are accomplished as per agreed project plan
• The test transfers are to be performed & all
discrepancies/inconsistencies should be resolved before receiving
production data transfer from vendor.
• Ensures that live data is acquired from third party vendor in a
timely manner during study conduct and closure phase
Role of Data Manager
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28.  Name the 4 types of TPV data (external data)
 Name 2 types of Lab and advantages of each
 Difference between Local Lab and Central Lab
 Name 2 Primary Key Variables and Secondary Key Variables
 State True or False -
 Bio analytical samples are analyzed to determine the pharmacokinetic
(PK) concentration of study molecule in a biological samples for a
particular study.
 Role of Data Loader
Test Your Understanding
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29. Questions
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30. In this session we have covered:
 Introduction to TPV Data
 Different types of Labs used in Clinical Trials
 Need and Significance of Lab Data
 Difference between Local and Central Lab data flow
 Key Variables
 Types of TPV Data
 How are TPV data reconciled
 Role of TPC
Summary
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31. You have successfully completed -
Handling Third Party Vendor Data