CDM essentials

Essentials of Clinical Data Management : 

Essentials of Clinical Data Management Dr Amit Bhatt 1

Today’s Purpose : 

Today’s Purpose This workshop has been designed to: Familiarize participants with the specific tasks involved in “traditional” clinical data management Provide participants insight from the perspective of a clinical data manager Enable participants to examine the processes, regulatory requirements and best practices 2

Today's Objectives : 

Today's Objectives Define the roles involved in clinical data management Identify reciprocal tasks at critical points before, during and post-study Practice using and becoming familiar with the required documentation Describe the consequences of poor clinical data management and the benefits of well-executed clinical data management List best practices in good clinical data management to avoid common mistakes 3

Slide 4: 

Define Individual Roles and Responsibilities 4

CDM Roles : 

CDM Roles Manager Data tracking Data coding Data entry Data validation Training Quality Control Quality Assurance 5

CDM Roles - Manager : 

CDM Roles - Manager Protocol review CRF development Authorization and database access Data validation document Approval of processes and procedures Oversight of all aspects of CDM 6

CDM Roles : 

CDM Roles Logging of paper CRFs Tracking data through CDM process, e.g. Completeness of CRFs CRFs through data entry process Data discrepancy forms Data Coding Adverse event coding Medication coding Data coding Review of CRF for accuracy 7 Data Tracking

CDM Roles : 

CDM Roles Data Entry Entering data Changing data Data Validation Developing CRF instructions Generating computerized or manual checks on a database to check for missing, inconsistent, or illogical data Implementing the data discrepancy management process Data Training Author CDM procedures Train personnel on procedures Create and maintain training documentation 8

CDM Roles : 

CDM Roles Quality Control Accuracy of data entry Implementing CDM processes and procedures including documentation Quality Assurance Conducts data audits Verifies that processes and procedures have been followed including documentation 9

Documentation : 

Documentation Each element of CDM requires, at least: Documented process Documented training Standard Operating Procedure (SOP) Work Instructions (WIN) Project Documentation Quality Control (QC) Quality Assurance (QA) 10

Team Collaboration : 

Team Collaboration Each functional representative on a team brings a unique set of experiences, skills, and knowledge Medical/Clinical Director & personnel CRA IT Statistics Data Management Programmers Data Entry The benefits & payoffs for the proper level of team involvement and inclusion are phenomenal! The project benefits The team members benefit 11

Establish Effective Communication : 

Establish Effective Communication Within the CDM team Between the CDM team and: Project Management Data Management Clinicians Statisticians IT Third parties, when applicable 12

Develop Research Plan : 

Develop Research Plan Overview Introduction Background Goals Clinical Plan Data Management Plan Statistical Analysis Plan Regulatory Considerations (if any) Human Subjects Protection 13

Develop Research Plan (cont) : 

Develop Research Plan (cont) Things to Remember: On-going communications On-going clinical/data/analysis decisions Written instructions for clinical process, data management, randomization, …) On-going documentation Goal is to preplan as much as possible No matter how well you plan … 14

Research Goal : 

Research Goal Ultimately, the desire is for others to believe the results of clinical research -- whether it be the general public, an industry-sponsor, NIH, DOD, the FDA, …. 15

So, How to Achieve this Goal? : 

So, How to Achieve this Goal? 16

Promote Integrity in Clinical Research : 

Promote Integrity in Clinical Research Clinical trial data must possess integrity to ensure confidence in analytical results. This requires: Research plan Quality standards and processes Validated systems Trained staff Multidisciplinary team Collaboration 17

Planning and Preparation : 

Planning and Preparation 18

Slide 19: 

19 CDM Study Activities

CDM Tasks – Study Start-up : 

CDM Tasks – Study Start-up Protocol Review Case Report Forms (CRFs) Review CRF Completion instructions CRF Annotations Data Management Plan (DMP) Data Validation Plan (DVP) Procedure (edit check) Requirements Lab Transfer Requirements (if receiving electronic data) 20

Slide 21: 

21 Sample CDM Study Checklist

Protocol Review - CDM : 

Protocol Review - CDM Overall consistency & clarity (all team members) Compliance with standards (all team members) CRF design perspective Database development perspective Data cleaning processes Coding considerations Considerations for electronic data transfers Randomization date and time specified It is NOT CDM’s responsibility to review for specific scientific content Details should be outlined in SOP(s)/WIN(s) 22

Case Report Forms (CRFs) : 

Case Report Forms (CRFs) CRF development is the first step in translating the protocol into data Ideally CRF development occurs concurrently with protocol development Collect the precise data required by the protocol Avoid collecting extraneous data Avoid collecting redundant data Avoid collecting derived data Easy to use (site personnel & data entry) Address the needs of those who have to work with the data Data Management & dB Development Statistics Clinical 23

Case Report Forms (cont) : 

Case Report Forms (cont) Use draft protocol to design CRFs: Standard CRF modules Project- and/or protocol-specific modules Draft CRFs to study team for review/input CRF review meeting Repeat draft reviews until no further changes Circulate final version for approval Coordinate CRF printing RDC studies provide user document Present CRFs & completion instructions at Investigator’s Meeting (may be done by CRA) Details should be outlined in SOP(s)/WIN(s) 24

CRF Design Tips : 

CRF Design Tips Data captured in only one place Fields clearly identified Consistent categorical fields Specify decimal point and number of places No calculated fields Appropriate review Pilot 25

Common Design Challenges : 

Common Design Challenges Evolving protocol development Lack of communication Electronic data sources Non-protocol related data Protocol amendments …. 26

Collect Precise Data : 

Collect Precise Data Patient visits: scheduled and unscheduled Medical windows Data collected at each visit Endpoints: primary, secondary, safety Randomization date and time Labs, imaging, … Physical exam Eligibility criteria Study withdrawal No extra data 27

Contents of Fields : 

Contents of Fields Dates, times: specify format include 24 hr clock if applicable Numeric data responses Few, if any, text fields Consistent coding (e.g., yes/no) Measurement units (e.g., in/cm, lb/kg, lab units, dosing units) Option of ‘unknown’ Collect ‘raw’ data Clearly labeled categorical fields 28

Concise, User-Friendly CRF : 

Concise, User-Friendly CRF Not enough can be said about this! Why? Remember those filling out the forms don’t know the data as well as you do Easy for the researcher to record the data Long term benefit (e.g., quicker turn-around, fewer data discrepancies) 29

CRF Completion Instructions : 

CRF Completion Instructions Document which provides clear instructions to site for accurate completion of the study CRFs Written by the CRA Start with “Standard Template” Study CDM reviews & provides input Keep in mind the: CDM Manual Review Checklist Data Management Plan (DMP) Electronic Procedures/edit checks These documents are not mutually exclusive (may be some overlap – but try to avoid unnecessary redundancies) Details should be outlined in SOP(s)/WIN(s) 30

Development of CRF Instructions : 

Development of CRF Instructions Easily accessed Less is better Key critical instructions to clarify fields Appropriate review Pilot 31

CRF Annotations (dB Specs) : 

CRF Annotations (dB Specs) CRF annotation (dB Specs) is the first step in translating the CRFs into a database application Annotations may include: Field names & attributes (length, data type, dictionary, etc.) Module/form names Data Extract view names Standard, existing modules annotated by study CDM Reviewed by CDM and Statistician Details should be outlined in SOP(s)/WIN(s) 32

Slide 33: 

33 Sample Annotated CRF

Development of a Data Management Plan : 

Development of a Data Management Plan Planning and Preparation 34

Elements of a DMP : 

Elements of a DMP Protocol Roles and responsibilities matrix including contact information Data flow diagram (DFD) Case Report Form (CRF) Annotated CRF Data validation plan (DVP) Scope of work for CDM 35

Elements of a DMP (cont) : 

Elements of a DMP (cont) Deliverables Data transfer specifications Don’t forget HIPAA Data closeout requirements Archiving process Project documentation including documentation of deviations from the DMP 36

Elements of a DMP (cont) : 

Elements of a DMP (cont) Quality and regulatory standards to be met and how they will be achieved Communication plan Applicable processes and SOPs/IOPs, including project specific processes and procedures Assignment of responsibilities for processes and procedures 37

Data Validation Plan : 

Data Validation Plan A user-defined comprehensive list of the edit checks and field calculations for the study Written by the CDM Start with “Standard” document/spreadsheet CRA reviews & provides input “Living” document throughout the study conduct Finalized at study closeout – for archiving Details should be outlined in SOP(s)/WIN(s) 38

Lab Transfer Requirements : 

Lab Transfer Requirements 39 Scope of Work document Define communication channels Define timing/frequency for file transfers File format Specifications for file transfers: Test names Visit names Date formats … etc. Must meet the requirements of any Lab Pre-Processor program Details should be outlined in SOP(s)/WIN(s)

ExerciseOutline a Data Management Plan for a Hypothetical Clinical Trial : 

ExerciseOutline a Data Management Plan for a Hypothetical Clinical Trial Participants will outline as a group 40

What is a CDMS? : 

What is a CDMS? A flexible relational database system for Capturing, Storing, and Processing clinical trial data 41 Design and Validate Database

Elements of Classic Validation : 

Elements of Classic Validation Requirements Design Verification Tests Procedures Training Installation Tests Periodic Tests Change Control Documentation 42

Elements of Classic Validation : 

Elements of Classic Validation User Requirements Design Verification Tests Documentation 43 Establish intended uses, what the software does, and is input to design and testing process How the software interacts with hardware to achieve requirements Testing to ensure that the system satisfies the design/requirements “If it isn’t documented, it didn’t happen!”

Elements of Classic Validation (cont) : 

Elements of Classic Validation (cont) Change Control Procedures and Training Installation Tests Periodic Tests 44 Software configuration management is key QA concern Required for each validation step Required to define User interactions In end-use environment with actual operating conditions Test system with know conditions with defined inputs and outputs

User Requirements : 

User Requirements Data flow graphic Annotated CRF Elements of external data sources Calculations for ‘raw’ data Required data validations Test data Analysis dataset requirements 45

User Requirements (cont) : 

User Requirements (cont) Deliverables Data transfers Analysis dataset requirements Reports Tracking system … 46

User Requirements (cont) : 

User Requirements (cont) Timeline Safety monitoring Site monitoring DSMB schedule Interim analysis, if applicable Final analysis … 47

Design : 

Design Platform specifications Data transfer mechanisms Database architecture CRF External data sources Report(s) design Tracking system specifications 48

Design (cont) : 

Design (cont) Security Physical System access Database access Data access Data transfers … 49

Verification Tests : 

Verification Tests Define tests for: Database Data elements Tracking system Reports … Implement tests for each 50

Procedures : 

Procedures Standard operating procedures Work Instructions Project specific procedures Training 51

Installation Tests : 

Installation Tests Database in system environment Tracking database in system environment 52 Periodically test the database, … to ensure that no changes have occurred if applicable Periodic Tests

Training : 

Training User Testing Data transfers Job responsibility … 53

Change Control : 

Change Control Define modification Conduct impact analysis Determine if change will be implemented Implement change Repeat testing when necessary 54

Remember: The Validation Flow : 

Remember: The Validation Flow 55

Documentation : 

Documentation User requirement Design All tests and test results User instructions Training Procedures Validation report System validation 56

Validation is …. : 

Validation is …. A process Required at the Operating Systems level Required at the Software level Required at the Applications level Required for human interactions at all levels 57

Validation Requires … : 

Validation Requires … Multi-disciplinary validation team 58

Why is this approach used? : 

Why is this approach used? Repeatibility and reproducibility Best practice for quality systems Regulations 21 CFR Part 11 59

21 CFR Part 11 : 

21 CFR Part 11 21 CFR Part 11 “Electronic Records; Electronic Signatures”, Effective August 1997 60

First Questions : 

First Questions 61 …but, what is Part 11? A regulation to control use of electronic record systems when these are used to comply with FDA regulations. …where does this information come from? The regulation itself The regulation Preamble Guide for use of Computers in Clinical Trials FDA Warning Letters

Basic Components : 

Basic Components Electronic Records Electronic Signature Validation 62

Electronic Records : 

Electronic Records Definition: “Electronic Record is any combination of text, graphics, data, audio, pictorial, or other information representation in digital form that is created, modified, maintained, archived, retrieved, or distributed by a computer system.” Meta-data 63

Electronic Records (cont) : 

Electronic Records (cont) Application of the regulation This regulation applies to “any records that are created, modified, maintained, archived, retrieved or transmitted, under any records requirements set forth in FDA regulation.” Predicate Rule 64

Electronic Signatures : 

Electronic Signatures Definition: “Electronic Signature is a computer data compilation of any symbol or series of symbols executed, adopted, or authorized by an individual to be the legally binding equivalent of the individual’s handwritten signature.” 65

Electronic Signatures (cont) : 

Electronic Signatures (cont) Requires TWO components: a separate action and a statement of action’s meaning User ID/Password at log on is NOT enough Certification letter submitted to FDA 66

Validation : 

Validation Validation is … 67 … processes which ensure that software conforms to its specification and meets the need of the user. … always required by Part 11. “Validation of systems to ensure accuracy, reliability, consistent intended performance, and the ability to discern invalid or altered records.” … use a graded approach Validation should reflect a safety-based approach to risk management.

Validation (cont) : 

Validation (cont) Application of Regulation “Validation of systems to ensure accuracy, reliability, consistent intended performance, and the ability to discern invalid or altered records.” WAIT!! What does this mean? 68

Common Myths : 

Common Myths FDA certifies computer systems FDA endorses the use of some software Others? 69

Guidances : 

Guidances Guidance for Industry Computerized Systems Used in Clinical Trials 21 CFR Part 11: Scope and Application Guidance 70

Safety Monitoring Plan : 

Safety Monitoring Plan On-going clinical review of adverse events and serious adverse events. Monitor recruitment Overall Site Evaluate site compliance Pre-planned data reports 71

Safety Monitoring Plan : 

Safety Monitoring Plan Potentially Reviewed by: Steering committee Site IRB Individual clinical review DSMB 72

Conduct and Management : 

Conduct and Management 73

Conducting and Managing a Clinical Trial : 

Conducting and Managing a Clinical Trial CRF Flow CRF Tracking Manual Review Data Entry Batch Data Load (BDL) Discrepancy Management Data Validation CDM Discrepancy Resolution – conventions, etc. Query Flow Coding (adverse events, medications) SAE Reconciliation Lab Data Review 74

Slide 75: 

75 CDM Process Flow (Paper-based study)

CRF Flow-Paper-based study : 

CRF Flow-Paper-based study 3-Part NCR [e.g., white, yellow, pink] CRA sends the white and yellow copies to CDM Pink copy stays at site CRF Tracking CRF pages logged within 24 hours of receipt All CRF pages stamped with the date received in house Forwarded to CDM 76

CRF Flow-Paper-based study (cont) : 

CRF Flow-Paper-based study (cont) Verify the CRFs are logged in correctly Identify and retrieve missing CRFs Forward white copies of the CRFs for archiving/scanning Forward yellow copies (“working copy”) to Data Entry with necessary notations and/or clarifications Verify patient initials and ID are the same for each page Check the spelling and legibility on text fields … etc. Details should be outlined in SOP(s)/WIN(s) 77

Data Entry-Paper-based study (cont) : 

Data Entry-Paper-based study (cont) Data Entry Guidelines – General and study-specific guidelines to enter data from CRFs into the database Double Data Entry Performed All data entered twice Second pass is verification/reconciliation Same Data Entry Operator cannot perform first and second pass entry on the same data CDM monitors ongoing data entry process Details should be outlined in SOP(s)/WIN(s) 78

Transition to e-CRF : 

Transition to e-CRF Requires a paradigm shift in thinking Requires careful planning Multi-disciplinary team Expert in paper CRF process Open mindedness Extra user training 79

Use of e-CRF : 

Use of e-CRF Impacts Sites and site start-up DMP Data validation CRAs and monitors Benefits Built-in tracking of CRFs and data discrepancies Data entry at site 80

Benefits : 

Benefits Quicker startup Reduced redundancy Improve data quality Reduced cost Enhances implementation 81

Efficient Methods : 

Efficient Methods Establish data flow Standardization Pilot Training Quality control implemented Quality assurance monitoring 82

Batch Data Load (BDL) : 

Batch Data Load (BDL) The process of inputting data from an electronic file, rather than through online data entry Multiple types of data possible (e.g., EKG, lab, patient diary) Receives cumulative or incremental files Data loaded into CDMS Derivation procedures are run to populate derived fields Develop and run edit checks Details should be outlined in SOP(s)/WIN(s) 83

Slide 84: 

84 CDM Process Flow-Paper-based study

Discrepancy Management : 

Discrepancy Management Note: this process may differ significantly for EDC studies Define data handling conventions Resolve discrepancies that arise during data entry Data validation executed to produce electronic edit check results on the data CDM also generates manual queries, generally on text fields; e.g.: Medications administered after baseline should have corresponding indications on Adverse Event If primary etiology is Diabetes, then Diabetes should be present on Medical History 85

Discrepancy Management (cont) : 

Discrepancy Management (cont) CDM performs discrepancy management Every discrepancy is reviewed to determine if it needs to be sent to the site or can be resolved in-house Discrepancies that do not require a query to be issued are closed Status in tracking system set to indicate that query will be generated Queries/DCFs are generated Status in tracking system set to indicate that query has been generated 86

Slide 87: 

87 CDM Process Flow-Paper-based study

Query Flow : 

Query Flow Note: this process may differ significantly for EDC studies Write clear, concise queries Data Clarification Form (DCF) Generation Documents queries that are sent Queries electronically/manually tracked internally Photocopy of query may be maintained internally Sending DCFs Status in tracking system set to indicate that query has been sent Send DCFs to site (via CRA or traceable mail) 88

Query Flow (cont) : 

Query Flow (cont) Returning DCFs Queries can be resolved by telephone, fax, email, or by site visit Completed signed queries sent back to sponsor DCFs stamped with the date received within 24 hours of receipt DCFs forwarded to the CDM Tracking DCFs Photocopy (yellow working copy) the resolved DCF Forward white copies of the DCFs for archiving/scanning Update query tracking spreadsheet Identify and retrieve missing DCFs Status in tracking system set to indicate that query has been received Review for completeness and validity 89

Slide 90: 

90 Returned DCF DOB=12-Sep-1945 See attached for corrected copy of CRF DR G Rose 21-Apr-2006

Query Flow (cont) : 

Query Flow (cont) Resolving Discrepancies Amend the database and the working CRF to correspond with resolutions on the DCF Status in tracking system set to indicate that query has been resolved\closed What if the query is not answered? A re-query will be issued Will follow the same query flow 91

Coding : 

Coding 92 Understand medical terminology and the structure of electronic dictionaries Adverse Events and Medications are coded using a system that provides a means to code verbatim terms to standard industry terms Ensure all adverse events and medications have been appropriately coded Coding reports are generated At the end of the study, verify all terms are coded Coding reports are reviewed/approved by the Medical Director (or designee) Details should be outlined in SOP(s)/WIN(s)

The Right Classification is Crucial : 

The Right Classification is Crucial Incorrect classification can mask drug affects or make drug affects appear when none are present The wrong classification of Adverse Events could have serious consequences for patients who later take the drug Failure to classify and report Adverse Events correctly to the FDA can cause a drug to be taken off the market or a company to be shut down 93

Why Classify Terms? : 

Why Classify Terms? 94 Before Classification Assume a study enrolling 300 patients 50% on treatment and 50% not After Classification

Understanding Coding : 

Understanding Coding Explanation of hierarchy of coding MedDRA: LLT → PT → HLT → HLGT → SOC WHO Drug: MP → PRG\PRT → ATC Adverse Events vs. medications Examples of coding process 95

SAE Reconciliation : 

SAE Reconciliation 96 Data on the Adverse Event CRF is compared to data from independent SAE database Carried out during the manual review of the CRF If any discrepancies, a query is generated If query results in a change to the SAE database, safety manager makes the change If query results in a change to study database, CDM makes the change Once all discrepancies are resolved the SAE database is final

Lab Data Review : 

Lab Data Review 97 Reports generated to ensure lab data is accurate Expected lab tests are present, lab unit conversions are correct New reports generated when new data is received Queries sent on discrepant data Lab Outliers Determine whether or not discrepancies for BDL lab outliers will be queried Outlier reports sent to the Medical Director for safety monitoring perspective which are reviewed/approved May be necessary to contact the provider (e.g. central lab) to confirm results or rerun samples Details should be outlined in SOP(s)/WIN(s)

Slide 98: 

98 CDM Tasks – Other Write department WINs and SOPs Standards ‘police’ (CRFs, dB modules, processes, templates, etc.) Provide internal training/coaching for DM team Interact with CRO for contracted DM tasks Understand roles and responsibilities of CRO vs sponsor Provide oversight and QC of CRO activities Recognize, communicate, and document changes in Scope of Work

Slide 99: 

99 CDM Tasks – Other (cont) Systems Understand architecture and functionality of clinical software applications Provide troubleshooting for system-specific problems Perform system-specific validation of new applications / releases Develop, approve, and/or execute user acceptance tests Requires understanding of regulatory guidelines 21 CFR Part 11 International Conference on Harmonization (ICH) Good Clinical Practices (GCP) Good Documentation Practices (GDP)

Slide 100: 

100 CDM Tasks – Other (cont) Develop & validate custom reports, utilities, etc. Batch Data Load (BDL) Process data transmissions from electronic sources Participate in the identification of & request for new CDMS features / enhancements / ‘bug’ fixes System support for clinical systems Active participation in professional organizations (e.g., OCUG, SCDM, SCT)

Close Out Tasks : 

Close Out Tasks 101

Close-Out : 

Close-Out Pre-QC Checklist Database QC Data Extracts to Statistics QA Audit Database Lock Archiving (Data, Docs) 102

Database QC (optional) : 

Database QC (optional) Pre-QC checklist Ensure all steps completed before QC is performed QC Audit Final Data Validation to ensure no further discrepancies Manually compare critical items from data listings to CRFs and DCFs % of all data points to be reviewed depends on the no. of patients & volume of data 100% QC done on Safety and Efficacy data QC should only be done on patients with no outstanding queries QC may be performed depending upon the number and significance of outstanding queries 103

Data Extracts : 

Data Extracts Create a ‘snapshot’ of the database (SAS format) This can be considered a ‘soft lock’ SAS database sent to Statistics Statistics creates data listings for QA audit Details should be outlined in SOP(s)/WIN(s) 104

QA Audit : 

QA Audit QA performs audit on all data If discrepancies found, database is updated, (another) final Data Validation is run CDM creates a new snapshot New datasets (final database) sent to statistics If no discrepancies, first QA snapshot is final database 105

Slide 106: 

106 All data entered and processed All coding of clinical events is complete Reconciliation between database and SAE system and/or any external data All queries resolved and database updated QC audit performed and issues addressed Pre-Lock Activities Final study visits complete Pre-Lock Activities

Definition : 

Definition Lock: When all clinical trial data has been reviewed, queries resolved and issues addressed, the database is closed or locked. The database cannot be changed in any manner after locking – unless an unlock has been performed (not optimum situation). Often, the amount of time it takes from the last subject visit to database lock can be a measurement of the study team’s efficiency. 107

Logistics : 

Logistics The database lock checklist is the main tool used by the clinical data manager to carry out database lock procedures. Exact checklist of procedures to follow before lock comes from the data management SOP, SSP or data management plan (DMP) 108

Processes : 

Processes Review and assure all coding of clinical events have been completed. Determine that SAE reconciliation has been completed. Ensure that there is agreement between the study medical monitor, biostatistician, and clinical data manager for data lock. Determine date following last subject visit for lock and manage this timeline effectively. 109

Processes (cont) : 

Processes (cont) Quality Control (QC) should: Audit the database for accuracy and completeness Provide the clinical data manager with any questions or comments. 110

Processes (cont) : 

Processes (cont) Clinical Data Manager should: Resolve QC concerns. Provide QC with resolutions and/or changes made to the database. QC audits changes and reports back to the clinical data manager on audit results. Clinical data manager obtains signatures on lock memo and informs programmer that the clinical research database is ready to be extracted for analysis. 111

Processes (cont) : 

Processes (cont) Programmer extracts data from clinical study database: Lock the clinical research database by request from clinical data manager and restricts all write-access to the clinical research data. Notify members of the clinical study team the date of the database lock. Create data exports or extracts of the clinical research trial data to support the analysis reporting by the biostatistician. 112

Processes (cont) : 

Processes (cont) Study Biostatistician performs preliminary analysis on the data: Conduct analysis and reporting according to the methodology described in the study’s Statistical Analysis Plan, Perform analysis using tested programs, Review output for data accuracy and layout of the tables, listings and figures, Report and coordinate with clinical study team members (as appropriate) on any data issues identified during analysis and reporting, If any additional analyses are required, the SAP is updated and new programs are developed and implemented, and Communication of analysis results to Clinical Study Team for reporting. 113

Database Lock Summary : 

Database Lock Summary Team collaboration is important for locking data appropriately and efficiently. Use of a checklist and proper documentation is essential. Ensure review time is understood and adhered to Measuring your trial efficiencies leading to and including locking the database can yield useful information for future study management. 114

Locking & Archiving : 

Locking & Archiving Ensure all pre-lock steps are accomplished All CRFs and DCFs received All discrepancies resolved All external data loaded All coding completed QA certificate submitted States that QA audit is satisfactorily completed Ensure no data has been changed after the last snapshot before the database is locked Database is locked and access limited to privileged users Create database lock memo Archive all study documentation 115

Slide 116: 

116 CDM Process Flow (paper-based study)

Post-Lock Activities : 

Post-Lock Activities 117 Lock and Post-Lock Activities Programmer locks data and notifies Biostatistician Biostatistician performs preliminary data analysis If necessary, unlock of database may be requested DM requests lock of database

Data Analysis and Reporting : 

Data Analysis and Reporting 118 Statistical Considerations

Database Reports and Statistical Analysis : 

Database Reports and Statistical Analysis Collaboration and early planning are necessary between CDM and statistics! Format and contents of reports typically require compromise to best optimize efforts for the two groups Many of the standard statistical reports could be done more efficiently by CDM Statisticians rely on CDM to ‘track down’ inconsistencies in the data 119

Prepare Data for Use in Clinical Reports : 

Prepare Data for Use in Clinical Reports First step for preparing data for use in clinical reports is incorporating discussion when the research plan is developed! Pre-planning for the needs of technical, safety, and DSMB reports as well as manuscripts is critical! Requires collaboration of CDM, statistics and clinical, and regulatory 120

Prepare Data for Use in Clinical Reports : 

Prepare Data for Use in Clinical Reports Requires pre-planning by each team member before coming together as a group Team needs to know, ahead of time, what templates are available for reports and any applicable requirements Team needs to decide what CDM platform will be used as well as define exporting requirements (e.g., Views, ASCII, SAS) 121

Prepare Data for Use in Clinical Reports : 

Prepare Data for Use in Clinical Reports Statisticians need to communicate their data structure needs clearly to CDM Team needs to know the DVP thoroughly (hopefully, members of the team helped develop the DVP) 122

Slide 123: 

Unblinding Definition of unblinding Why studies are protected from unblinding during the study Reasons for unblinding during the study and at the conclusion of the study Documentation for unblinding Importance of unblinding at the end of the study 123

Blinding Treatment Assignments : 

Blinding Treatment Assignments Blinding a study’s randomized treatment assignments is vital in controlling the potential treatment biases of study participants and study personnel. During the development and conduct of the study there are many ways that the treatment assignments may purposely or incidentally become unblinded. Unblinding during the trial may jeopardize the scientific integrity of the study. To minimize this risk there are a variety of procedural steps that can be taken to ensure that the likelihood of incidental unblinding is minimized. 124

Unblinding Treatment Assignments : 

Unblinding Treatment Assignments Unblinding SOP will be in place, fully written and approved by the study sponsor and appropriate committees, prior to entry of the first participant. Procedures describe unblinding of: individual participants for safety purposes, entire study for interim analysis and reporting, entire study at completion and final analyses, and informing participants of their group assignment. 125

Unblinding Entire Study at Completion and Final Analyses : 

Unblinding Entire Study at Completion and Final Analyses Individuals involved in endpoint assessment should not be informed of the treatment assignments prior to the lock of the data sets. No changes should be made to the data after the dissemination of the treatment assignments. 126

Unblinding : 

Unblinding Should occur in a consistent and controlled manner SOP describes processes for breaking the statistical blind in a work flow process. Roles and responsibilities should be clear. Properly document the break (signatures and dates). 127

Unblinding Summary : 

Unblinding Summary Three Key Elements: Descriptions of when and when not to unblind, and authorized personnel Established processes Proper documentation 128

Clinical Study Report Description : 

Clinical Study Report Description A CSR can be described as: A written report that integrates information from the clinical protocol, statistical methods and analyses, and the results of the human clinical trial. Developed in accordance with ICH Guidances. 129

Important Guidance Documents : 

Important Guidance Documents ICH E6 Good Clinical Practice: Consolidated Guidance: A written description of a trial/study of any therapeutic, prophylactic, of diagnostic agent conducted in human subjects, in which the clinical and statistical description, presentations, and analyses are fully integrated into a single report. ICH Topic E3: Structure and Content of Clinical Study Reports 130

Purpose of the CSR : 

Purpose of the CSR Describes and interprets the clinical study for the regulatory reviewer. Synthesizes the study objectives, methods and endpoints, interprets the results and includes the conclusions that justifies the choices made in the protocol and significance of the findings. 131

Important Requirements : 

Important Requirements A complete report enables someone who is not familiar with the study to review and understand the details. CSRs therefore must be: Concise and consistent Well organized Easy to follow and read (cross linked appropriately and formatted) 132

General Overview of Clinical Study Report : 

General Overview of Clinical Study Report Key Elements of the CSR: Title Page Synopsis Table of Contents List of Abbreviations and Definitions of Terms Ethics 133

General Overview of Clinical Study Report : 

General Overview of Clinical Study Report Key Elements of the CSR: Investigators and Study Administrative Structure Introduction Study Objectives Investigational Plan (Methods) Study Subjects and Treatment Information 134

General Overview of Clinical Study Report : 

General Overview of Clinical Study Report Key Elements of the CSR: Results (Efficacy Evaluation and Safety Evaluation) Summary and Discussion Overall Conclusions References Supporting Data Appendices 135

General Overview of Clinical Study Report : 

General Overview of Clinical Study Report Appendices Batch numbers by subject Discontinuation of subjects Key demographics and baseline characteristics Key efficacy/PK/PD by subject Adverse Events Deaths Non-fatal SAEs Discontinued study due to AEs Medical Labs/Vital Signs/ECG Abnormalities Other pertinent attributes 136

General Overview of Clinical Study Report : 

General Overview of Clinical Study Report Paper vs. Electronic Most organizations have an electronic CSR template Business rules (organization style guide) are applied for formatting, displays and headings Electronic publishing is an important component of “e” study reports and is a function of a document management system (future) 137

General Overview of Clinical Study Report : 

General Overview of Clinical Study Report Development of CSR Medical writer uses an approved template Meets with clinician and biostatistician at a minimum in advance of the data outputs Develops many sections in advance Standardizes across a program Agreement on list of tables, listings and figures 138

Clinical Data Management Areas of Focus : 

Clinical Data Management Areas of Focus Clinical data management and QC reviews the entire report Comments regarding findings are reviewed with the medical writer Verifies any changes in the conduct of the study or planned analyses Reviews efficacy results and tabulations of individual subject data Reviews appendices 139

Summary : 

Summary 140 Team collaboration is key! Content must be consistent, concise and well organized Complete document Efficacy results and tabulations of individual subject data Tables, figures and graphs referred to but not included in the text Appendices

Promoting EfficiencyinClinical Data Management : 

Promoting EfficiencyinClinical Data Management 141

Continual Process Improvement : 

Continual Process Improvement Promote efficiency Reporting and Metrics Avoid common mistakes Trained and motivated staff Commitment to best practices/standards CDISC Standardize database and CRFs when possible Promote team communication Commitment to Quality Assurance 142

Promote Efficiency : 

Promote Efficiency 143 Essential component of Clinical Data Management Develop ad-hoc reports for clinical team as requested Identify and follow up inconsistent data points Statistics on performance Reporting and Metrics

Promoting Efficiency : 

Promoting Efficiency Analyzing measures of efficiency can provide process improvements that increase data quality for future studies. This includes: Total number of discrepancies Percentage of discrepancies resolved “in house” Percentage of discrepancies resolved via site data modifications Top 5 or 10 discrepancies Average time to resolve queries Time from last query resolved to study lock 144 Reporting and Metrics

Promoting Efficiency : 

Promoting Efficiency Poor database design Poor CRF design Missing items—times, dates, etc. Poor coding Generic and brand name drugs coded differently Unnecessary queries Insufficient data checks Expensive to resolve Untrained staff Statistical analysis of “dirty data” Inadequate company standards Naming conventions, documentation, etc 145 Prevent Common Mistakes

Promoting Efficiency : 

Promoting Efficiency Review training programs on a regular basis. Ask trainees for input on how to make training program better. Give staff the opportunity to present relevant data management topics at meetings. Reward staff for above average performance. Give your staff the training resources they need to perform their job effectively. 146 Trained and Motivated Staff

Slide 147: 

147 MISSION: To develop and support global, platform-independent data standards that enable information system interoperability to improve medical research and related areas of healthcare. CORE PRINCIPLE: Lead the development of standards that improve process efficiency while supporting the scientific nature of clinical research. CLINICAL DATA INTERCHANGE STANDARDS CONSORTIUM Commitment to Best Practice

Slide 148: 

148 SDTM is an example of the promotion of the gold standard for study data naming conventions CDISC Study Data Tabulation Model (SDTM) and implementation guide available at www.cdisc.org What is the SDTM? STUDY DATA TABULATION MODEL Commitment to Best Practice

Promote company standards : 

Promote company standards Standard naming conventions Standard CRFs Templates-DMPs, SAPs, etc Standard databases Increase reproducibility 149

Slide 150: 

Loss of data integrity Invalid study Frustrated team Angry sponsor Wasted time & $$$ Unemployed team Data integrity Efficient processes Treatment to market Intervention to patients Publications Happy personnel Job security 150 Good CDM Poor CDM CONSEQUENCES OF GOOD VS POOR CLINICAL DATA MANAGEMENT

Promote team communication : 

Promote team communication Each functional representative on a team brings a unique set of experiences, skills, and knowledge Medical/Clinical Director & personnel CRA Statistics Data Management Programmers Data Entry Ensure that the team is communicating on a regular basis via meetings, teleconferences, emails, reports, etc. The benefits & payoffs for the proper level of team involvement and inclusion are phenomenal! 151

Summary : 

Summary Clinical Trial: a complicated process, made possible via: Teamwork / Team reviews Communication Standards Continual process improvements Participation Involve CDM early & throughout Keys: Quality … Quality … Quality! Standards … standards … standards! Team work … team work … team work! 152

Slide 153: 

153 Questions