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Study Set-up: Study Set up: The Roadmap For Effective Data CollectionData Collection21st January 2011
Indrani KakadeHead, LS BPO Training, Quality & Compliance
0©2011, Cognizant
0
Contents
Introduction
Essential components of study set-upp y p
Key stakeholders, Roles and Expectations
Methods to meet Expectations & Benefits
Potential failure points and Impact
Case Study – Process Streamlining
Process Improvements
Case Study - SSU Accelerators
1 | ©2011, Cognizant 1
IntroductionA story about a sports event in India …………….
Investigating Committee Event Managers
Why is the length of the racetrack not as per requirements for the 100 meter race?
Oops!! We did not think of all the possible races
Why don’t we have hockey equipment ?
Where are the Drug test results?
Hockey? We have hockey too in this event?
Last I remember, they were lying somewhere Where are the Drug test results?
Why do we have so much cricket equipment lying around?
in the lab …
There’s cricket all around .. I assumed it would be a part of this event too ..
Why is this 150Kg boxer pitched against this 50kg boxer
We will have a clear winner. Just trying to get things done faster ..
2 | ©2011, Cognizant 2
MURPHY’S LAW is always RIGHT !!!!
Essential Components Of Study Set-UpFinalized protocol is required to commence any activity of study set-up
CRF Designing• This activity involves creating study specific CRF as per protocol such that
the data is collected in standard format for analysis of primary and secondary objectives of the trial
Finalized protocol is required to commence any activity of study set up
y j
DMP & Documents Development
• DMP document specifies the process for handling and validating the data
Data Validation Specification
• Data validation specification document enlists the specifications to be programmed as edit checks in the database
p
IVRS , Lab & other 3rd party Setup and
I t ti
• IVRS is used for patient randomization and drug supply management• Central & Local labs for sample assessment • e PRO for QOL Biomarkers ECG etcIntegration • e-PRO for QOL , Biomarkers , ECG etc.
Database Build, Testing and UAT
• Building DB as per specifications (CRF, DVS, Integration specs)• To ensure validation & testing of databases and integrations
3 | ©2011, Cognizant 3
Testing and UAT
Key Stakeholders – Roles and ExpectationsData Entry
Expectations
Data Manager
Expectations• All checks firing as required
Expectations• All forms/modules are created as per
protocol and CRF• Data Entry Guidelines
Role : Testing of DB
Auditor
Expectations• Compliance to regulatory g q
• Database set-up as specified in ‘Requirement document’
Role• Review of ECS• Review of specifications• UAT
Compliance to regulatory requirements and SOPs
• Complete, consistent and accurate documentation
Set-up Team Investigator & Monitors Expectations• User – friendly interface• No misfiring discrepancies
Clinical TeamExpectations• All data as per protocol is capturedRole g
• No duplicate queries • Finalized protocol• Review of all documents
Statistician ProgrammerStatistician
Expectations• DB collects the expected data as per
protocol • All variables are accurate (units etc.)Role• Inputs to ECS
Programmer
Expectations• All data required for analysis as
specified in CRF is collected• Clear specifications Role• Review and Inputs to protocol
4 | ©2011, Cognizant 4
Inputs to ECS• Review and Inputs CRF
Review and Inputs to protocol• Review and Inputs CRF
Methods to meet Expectations and BenefitsMethods Benefits
Standard Libraries• To maintain a bank of global and therapeutic
area wise standard modules and checks
• Standard checks across studies• Increased reproducibility hence saving of time and effort • Save time in testing
St d d T l t C l t d if d t ti t diStandard Templates• Standard templates for study documents like DMP,
Data entry guidelines , Edit check specifications etc.
• Complete and uniform documentation across studies• Mistake proofing • Avoid audit findings• Saving of time
Quality Control • Identification of early failure points y• Review by a Peer/Senior study member at each
step
y p• Reduction in re-work• Improved quality
Continuous training, sharing of best practices and lessons learnt
• Accelerated learning curve Reduce knowledge gapsand lessons learnt
• Common understanding across the process• Reduce knowledge gaps• Increased efficiency
Query Text Standardization• Incorporating standard query texts in libraries for
• Standard queries across studies• Minimum errors by data managers
standard modules and checks • Increased satisfaction from Investigators • Reduce re-queries
Timely inputs from study team and Understanding expectations of all stakeholders
• Clear understanding and incorporation of inputs
• Customer satisfaction• No surprises after Go-live
5 | ©2011, Cognizant 5
• Clear understanding and incorporation of inputs provided by all stakeholders right at study start up phase
Potential Failure Points and Impacts
Examples of possible failure points and its impact
Scenarios Impact
Critical variable missing in the CRF/eCRF • Database Migration/Downtimedesign /output specifications
• Incomplete Analysis and Outcome of the trial
• Rework for set-up teamLatest Protocol amendment not referred for designing CRF
• Extension of Go-Live date
• Database Unlock
M l l i f d t b D t M
designing CRF
Incorrect format specified in specifications or tested incorrectly during the UAT
• Manual cleaning of data by Data Manager
• Re-testing and Re-work leading to delayed timelines
• Unhappy team members and stakeholders
Misfiring edit checks
ppy
• Loss of credibility & Relationships
• Budgetary Impact
Duplicate or multiple checks
6 | ©2011, Cognizant 6
Examples of Process ImprovementsImprovements
7 | ©2011, Cognizant 7
Case Study – Streamlining of Documentation in Start-up Process
Problem Statement
D l i G li ti li d t hi h b f DM S t
• Process Streamlining was initiated using principles of Lean and Process Improvement
Situation• 22 unique DM Set up documents
Solution• Simpler Streamlining process with less
Benefits• Reduction in redundant & duplicate information
• Delay in Go-live timelines due to high number of DM Set up documents capturing repetitive information requiring multiple reviews and sign offs
• 22 unique DM Set up documents• Repetitive information across multiple
documents• Multiple reviews and numerous
comments & updates• Challenge to review CRF using non
visual documents
• Simpler Streamlining process with less documentation (10)
• One eCRF Specification document introduced which replaced many documents
• Direct data entry of test scenarios in Rave without need for prior entry into
• Reduction in redundant & duplicate information, hence no issue of consistency in information across different documents
• Speedy delivery of documents• eCRF Specification document is more intuitive as it
gives a visual representation of CRF and serves as a single source document for reference
• Complex specification writing in test scripts
• Dissatisfaction from data management study team members
• Replication of Legacy database process
p ytest script (reusing test scripts & data)
g• Due to reduction in documents , the teams have
more time to focus on critical information captured• Due to direct data entry , the dependency and proof
of testing is on automated reports thereby minimal reconciliation
D i i 26%
8 | ©2011, Cognizant 8
Document review time
Other critical activities
45% 55%26%
74%
• Define an efficient process for the development of edit checks
Case Study – Study Set Up Accelerators
Problem Statement
D l i ti li d t i d l
Goal• Define an efficient process for the development of edit checks
including specification, testing and inclusion of dynamic query Messaging
• Avoid the need to re-program individual edit checks for every study (e.g. customizing ranges, query messages etc.)
• Delay in timelines due to increased manual effort (create and test edit checks and the
associated data query messages ) during Rave study setup
Situation
• Repetitive manual effort required to create and test edit checks
Solution
• Accelerator was developed to facilitate and automate the
Benefits
• Effort reduction in terms of – DVS preparationto create and test edit checks
• Repetitive manual effort for data query message wording and translation issues
• Time invested in DVS creation
facilitate and automate the following
– Data validation specification– Development of edit checks– Creation of test scripts and
DVS preparation– Edit Checks Development– Test Scripts Preparation and Test Data– Query Development time
• Fewer Errors/Issues resulting in High StudyTime invested in DVS creation was too high
Creation of test scripts and test data in CDISC or sponsor format
– Edit Checks from Global Library and Sister Study
Fewer Errors/Issues resulting in High Study Built Quality
• The tool as such is platform and standards independent
• Fewer resources required for each studyy y– Minimize Edit check
customization to changing of Query text, Folder, Form names
q y• This accelerator gives easy way to add the
Edit Checks from Global Library and Sister Study
• Dynamic query messaging
9 | ©2011, Cognizant 9
y q y g g