data_management - dipen khanna
TRANSCRIPT
21 April 2005
Introduction to Introduction to Data Management Data Management
DIPEN KHANNA
Manager – Data Review
Pfizer Pharmaceutical India Pvt. Ltd.
21 April 2005
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…
21 April 2005
What is Data Management?What is Data Management?
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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What is Data Management?What is Data Management?
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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What is Data Management?What is Data Management?
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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Data Management ResponsibilitiesData Management Responsibilities
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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Data Management ResponsibilitiesData Management Responsibilities
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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Data Management ResponsibilitiesData Management Responsibilities
Study Conduct:Participate in dictionary coding
– Adverse Events– Medications– Medical History
Request and track batch validationsOversee electronic data loads
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Data Management ResponsibilitiesData Management Responsibilities
Study Conduct:Perform CRF page trackingOversee data flow Participate in blinded data reviews (BDRs)
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Data Management ResponsibilitiesData Management Responsibilities
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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How Does DM Fit into Clinical Research?How Does DM Fit into Clinical Research?
• 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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How Does DM Fit into Clinical Research?How Does DM Fit into Clinical Research?
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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The CRF or Other Data Collection Tool (DCT)…The CRF or Other Data Collection Tool (DCT)…
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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The CRF or Other Data Collection Tool (DCT)…The CRF or Other Data Collection Tool (DCT)…
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!!!