study set-up: the roadmap for effective data collection

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 ..


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


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


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


• 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


Examples of Process ImprovementsImprovements


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%


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


y q y g g