Data_Management - Dipen Khanna

21 April 2005

Introduction to Introduction to Data Management Data Management

DIPEN KHANNA

Manager – Data Review

Pfizer Pharmaceutical India Pvt. Ltd.

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What is Data Management?What is Data Management?

Historically, Data Management has been thought of as “running edit checks” and “writing queries”.

While these two aspects of Data Management still exist as very important functions, Data Managers are responsible for many other aspects of managing the data, which may include…

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Setting up complex checks in either the Clinical Trial database or Data Browser programs

Managing a vendor who is performing the data management for a study

Interacting with other specialty outsource vendors (e.g., Central Labs)

Acting as a key contributor to the larger Clinical Project team

Oversight of Case Report Form and database development Knowledge of several complex systems for overseeing the

data and data quality (e.g., OC, OC RDC, etc.) Others???

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Defined simply, Data Management is…

The entire process involved with taking original raw data from the clinical sites and compiling and validating it, so that it is suitable for reporting.

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Ways in which data are managed:

Monitoring and Source Document Verification Validation checks within a database EDC auto-hits at the clinical site Manual data checks Double data entry of all CRF data Blinded data review (BDR) Issue and resolve site queries Code Adverse Event & Medication terms Review of data listings Database QC audit Others???

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Data Management ResponsibilitiesData Management Responsibilities

Study Start-up:Coordinate Protocol and CRF developmentDevelop data management documents

– CRF Completion Guidelines– Data Management Plan – Self-evident Corrections – Data Entry Guidelines

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Study Start-up:Oversee database development

– I*NET– Oracle Clinical

Identify, oversee development, and test validation procedures

Oversee Imaging system set-upPerform/oversee lab set-up

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Study Conduct:Maintain data management study

documents and ensure they are in the TMFOversee data management activities

performed by a CRO/FSP and provide necessary study specific documents

Generate, distribute, and resolve queries

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Study Conduct:Participate in dictionary coding

– Adverse Events– Medications– Medical History

Request and track batch validationsOversee electronic data loads

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Study Conduct:Perform CRF page trackingOversee data flow Participate in blinded data reviews (BDRs)

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Study Close-out:Test break blind program Oversee QC AuditsPerform database release and re-releaseDocument post-database release changes

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How Does DM Fit into Clinical Research?How Does DM Fit into Clinical Research?

• The data management function supports/oversees all data collection and data validation for a clinical trial program.

• Data management is essential to the overall clinical research function, as its key deliverable is the data to support the submission.

• Assuring the overall accuracy and integrity of the clinical trial data is the core business of the data management function.

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• Data management starts with the creation of the study protocol

• At the study level, data management ends when the database is locked and the Clinical Study Report is final

• At the compound level, data management ends when the submission package is assembled and complete

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ICH Guidelines for Good Clinical Practice list requirements for how clinical trial data shall be validated and updated. ICH GCP 5.5 ICH GCP 8.3.14 ICH GCP 8.3.15

Example:5.5.1 “The sponsor should utilize appropriately

qualified individuals to supervise the overall conduct of the trial, to handle the data, to verify the data, to conduct the statistical analyses, and to prepare the trial reports.”

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Additional Guidelines -

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The quality of a clinical study isThe quality of a clinical study isonly as good as the weakest data only as good as the weakest data point…point…

Importance of Effective Data Management

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Importance of Effective Data ManagementImportance of Effective Data Management

Statistical analysis – an accurate database is the basis for drug approval by the FDA– Adherence to federal regulation and guidelines mandate the safety

and welfare of patients participating in trial and ultimately, the safety of patients prescribed the approved drug

Marketing the drug– It is important for patients and physicians to clearly understand the

indication for the treatment, potential side effects, and contraindications for the use of the product

Post marketing surveillance– Drug companies are required to send safety information to the

FDA after the drug has been approved and marketed

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Ensuring Quality DataEnsuring Quality Data

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The CRF or Other Data Collection Tool (DCT)…The CRF or Other Data Collection Tool (DCT)…

We all know the saying…

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Protocol to CRF:• The study protocol dictates what data need to be

collected.

• The CRFs or DCTs should be designed to collect only the data required to answer the study protocol’s research question(s).

• Collecting the “nice to have” data that is not specified in the protocol should be avoided.

• Standards should be adhered to.

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CRF to database:• The CRF defines the overall design and structure of

the clinical trial database.

• Data should only be collected in one place. Multiple sources of the same data introduces additional possibilities for error.

• If a data point must be summarized it must be captured as a numeric or coded value. Free text cannot be summarized.

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The Clinical Trial DatabaseThe Clinical Trial Database

Oracle Clinical (OC):Storing and validating the clinical trial data.An industry standard for managing clinical trial data.

OC is a fully validated system:Conforms to the software development life cycle (SDLC)

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The Clinical Trial DatabaseThe Clinical Trial Database

Excel spreadsheets or Access databases should never be used to capture the clinical trial data, as they are not validated for that purpose and do not conform to GCP Guidelines.

• Excel and Access are not set up to require 1st and 2nd pass data entry

• Excel & Access have no audit trail to track data changes

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Oracle Clinical StructureOracle Clinical Structure

Oracle Clinical

OC is set up to allow for easy data entry and data retrieval.The data entry screens are actually set up in sequential order of the CRFs, so that a Data Entry operator can enter the data quickly.

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Data VerificationData Verification

Verification: check that what is in the source doc is on the CRF and what is on the CRF is in the database

Verification ensures that data are reported accurately on the CRFs and are consistent with the source data.

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Data VerificationData Verification

Data Verification is performed: – At the site via source document

verification– In-house via:

Double data entryData reviews – BDRs, listings, etc.

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Data ValidationData Validation

Validation: check that what is in the database is logical, consistent, and analyzable

Validation ensures that data are:CompleteCorrect Allowable ValidConsistent

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Data ValidationData Validation

Data Validation is performed:– At the site via CRF review for consistency

and validity– In-house via:

Programmed data checks within Oracle Clinical

Manual data review via listing or edit check

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Verifying and Validating the DataVerifying and Validating the Data

Potential Sources for Error– People– Data entry– Coding process

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Verifying and Validating the DataVerifying and Validating the Data

Possible Types of Error– Erroneous Data– Protocol Deviations– GCP Violations

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Data Management DocumentationData Management Documentation

Data Management Plan may include:– Lists checks performed– Identifies which discrepancies can be solved

in-house (self-evident changes or no action required)

– Identifies which must be queried to the investigator

– Lists any assumptions that can be made during review/coding process

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Data Management Documentation

Self-Evident Corrections (SEC):

–Lists all changes that can be made to the data by sponsor without a query to the Investigator

–Site is sent document prior to start of discrepancy management and at least one more time once the study has ended

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When to Query the SiteWhen to Query the Site

• Only a very limited number of corrections can be made to the data, without querying the clinical site.

• For any data discrepancies that cannot be corrected as self-evident and are clearly data errors, a query, or Data Clarification Form (DCF) must be sent to the site.

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Data QueriesData Queries

Definition– Individual questions sent to investigative site

concerning a data discrepancy – Should be generated on ongoing basis– Should be resolved as early as possible– Query cycle time consuming and expensive

Commonly quoted each query cost $50-$75 to resolve

Query generation/resolution typically takes up 50% of the total data processing time

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Data QueriesData QueriesData Discrepancy Flow

Data Discrepancy

Generate Query

Send to Site

Site Responds with Answer

Data Manager makesChange to Database

Self-Evident Correction

Data Manager makesChange to Database

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When are the Data Considered Clean?When are the Data Considered Clean?

• All data has been received and entered

• All DCFs and OC discrepancies have been addressed and resolved

• A final QC has been performed across the entire study database

• At this point the database may be locked and unblinding information is added.

• Checks are run to validate the unblinding information.

• The database is then frozen and released for analysis.

• All SAEs are reconciled with the safety database.

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Database ReleaseDatabase Release

Making any post-release changes is highly discouraged, unless significant data issues are identified.There are very clear & detailed procedures on how to make a post-release change, should it be necessary.

Oracle Clinical

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A123456 Listing of Best Subject Response with Demographic DataSubject Initials Age Sex Race Treatment Best Response Duration of ResponseABC 56 M Caucasian Our Drug Stable Disease 36 weeksDEF 48 F African-American Our Drug Partial Response 22 weeksGHI 62 F Asian Our Drug Partial Response 19 weeksKLM 46 F Caucasian Competitor Drug Partial Response 13 weeksNOP 53 M Hispanic Competitor Drug Stable Disease 17 weeksRST 38 M Asian Our Drug Partial Response 35 weeksUVW 59 F African-American Competitor Drug Progressive Disease N/A

Reporting the Data---Data Out

• This is a sample listing for the fictitious Oncology study A123456.

• This may be one of many tables used to perform the overall analysis of this trial’s data.

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SummarySummary

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Protocol DesignProtocol DesignProtocol DesignProtocol Design CRF DesignCRF DesignCRF DesignCRF Design Database setup Database setup and validation and validation procedures procedures

Database setup Database setup and validation and validation procedures procedures

Detailed statistical Detailed statistical analysis plan writtenanalysis plan writtenDetailed statistical Detailed statistical analysis plan writtenanalysis plan written

Monitored CRFs Monitored CRFs receivedreceivedMonitored CRFs Monitored CRFs receivedreceived

ScanningScanningScanningScanningIndexingIndexingIndexingIndexingData entryData entryData entryData entry

Data verificationData verificationData verificationData verification Data Entry auditData Entry auditData Entry auditData Entry auditMerge data into Merge data into databasedatabaseMerge data into Merge data into databasedatabase

Online dictionaries Online dictionaries automatically automatically appliedapplied

Online dictionaries Online dictionaries automatically automatically appliedapplied

Update database Update database with resolutions and with resolutions and close out queriesclose out queries

Update database Update database with resolutions and with resolutions and close out queriesclose out queries

Send to sites thro’ Send to sites thro’ CRAs/ DMsCRAs/ DMsSend to sites thro’ Send to sites thro’ CRAs/ DMsCRAs/ DMs

Generate queries Generate queries Generate queries Generate queries Batch validationBatch validationBatch validationBatch validation

Tables generatedTables generated

100% QC of tables100% QC of tables

Tables generatedTables generated

100% QC of tables100% QC of tables

Programming for Programming for all safety & efficacy all safety & efficacy tables completedtables completed

Programming for Programming for all safety & efficacy all safety & efficacy tables completedtables completed

Database closure for Database closure for reportingreportingDatabase closure for Database closure for reportingreporting

Database auditDatabase auditDatabase auditDatabase audit

Issue of final Issue of final biometrics tables biometrics tables and report textand report text

Issue of final Issue of final biometrics tables biometrics tables and report textand report text

Review of reports, Review of reports, tables by clinical tables by clinical and biometrics and biometrics teamsteams

Review of reports, Review of reports, tables by clinical tables by clinical and biometrics and biometrics teamsteams

Statistical report Statistical report on methodology on methodology and findingsand findings

Statistical report Statistical report on methodology on methodology and findingsand findings

Flow Chart of ActivitiesFlow Chart of ActivitiesFlow Chart of ActivitiesFlow Chart of Activities

CSRCSR

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Data Management WorkflowData Management WorkflowDefine project specific data management

requirementsWith Study Start Up:

– Develop CRFs – Develop database– Develop validation checks

CRF computerized tracking, entry and verification

Tracking electronic data loads

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Validation checks and data listings generatedData review, coding, and query generationQuery resolution and database changesFinal database updates and verificationFinal database quality checkDatabase lock and delivery to reportingPost-release change management

Data Management WorkflowData Management Workflow

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Whew!!!

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Q & A

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Thank you for your participation today!!!

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Data_Management - Dipen Khanna