Data Validation

Data Validation Kirtikrushna:

Data Validation Kirtikrushna

Processes Involved:

2 2 Processes Involved There are two processes involved: Data Validation/Editing – Review data for completeness, accuracy, and logic Discrepancy Resolution Verify or correct incomplete and/or inaccurate data

Who Cleans Database?:

3 Who Cleans Database? DMP clearly defines tasks & responsibilities involved in database cleaning through SOP(s) Please see SOPs… who is Responsible for Cleaning!

Data Validation:

Data Validation

Data Validation :

5 5 Data Validation To validate data means to review the CRF image and the eCRF (database) for accuracy and completeness . The Protocol will provide a definition of what data is expected. The CDM Standard Guidelines for Editing Data and Project Specific Guidelines will provide the action to be taken for data problems identified during CRF Point-by-Point/Page-by-Page review and by the validation programs.

Data Validation:

6 6 Data Validation Documents are received continuously throughout the duration of the clinical trial and the data is entered into the database as it is received. A continual review of the data is required to ensure obvious data errors, inconsistencies, data that is not meaningful, omitted data or additional hand written data are identified. This ensures data accuracy and completeness.

First Pass Editing Queue:

7 7 First Pass Editing Queue The First Pass Editing Queue contains the documents that have been through First Pass and Second Pass Data Entry as well as Batch Validation.

Cleaning Data:

8 Cleaning Data Activities include: Manual review of data Addressing the computer generated checks/validations designed to identify discrepancies Logical & Statistical checks to detect impossible values/issues via listings

Slide 9:

Point-by-point checks Textual data Continuing events Query generation Query integration Missing data Duplicate data Protocol violation Coding SAE Reconciliation Data consistency Ranges External data Visit sequence DATA CLEANING

Cleaning Data:

10 Cleaning Data Types of activities include: Data entry errors - Making sure that raw data were accurately entered into a computer-readable file Checking for Completeness - Eliminating duplicate data entries Missing values for variables where complete data are necessary Uniqueness of certain values, such as subject IDs Invalid or inaccurate data values Invalid or inaccurate date sequences

Cleaning Data:

11 Cleaning Data Consistency/Continuity checks - Verifying that complex multi-file (or cross panel/multiple data points) rules have been followed e g., if an AE of type X occurs, other data such as concomitant medications or procedures are expected Detect strange patterns in data character variables to contain only valid values numeric values are within predetermined ranges Coding Protocol violations

Data Validation:

12 12 Data Validation In addition to validating data, the data editing staff will monitor: Which pages are received and which are still missing Data errors that need site communication for resolution Data that is received from outside sources (e.g. Lab) Study milestones that require progress reporting

Clean Data Checklist :

13 Clean Data Checklist List of checks to be performed by data management while cleaning database Checklist is developed & customized as per client specifications Provides both ongoing/periodic checks end of study checks Strict adherence to checklist prevents missing out on any of critical activities

Point-by-Point Checks/ Page by page review:

14 Point-by-Point Checks/ Page by page review Cross checking between CRF & database for every data point Constitutes a “second-check” apart from data entry Incorrect entries/entries missed out by Data Entry are corrected during cleaning Special emphasis to be given for Dates Numerical values Header information (including indexing)

Missing Data Checks:

15 Missing Data Checks Missing responses to be queried for, unless indicated by investigator as not done not available not applicable Validations to be programmed to flag missing field discrepancies Missing Data…!!

Missing Page Checks:

16 Missing Page Checks Expected pages identified during setup of studies Tracking reports of missing pages to be maintained to identify CRF(s) misrouted in-house CRF(s) never sent from Investigator’s site AE Form

Protocol Violation Checks:

17 Protocol Violation Checks Protocol adherence to be reviewed & violations, if any, to be queried Primary safety & efficacy endpoints to be reviewed, to ensure protocol compliance

Key protocol violations :

18 Key protocol violations Inclusion & exclusion criteria adherence Age Concomitant medications/antibiotics Medical condition Study drug dosing regimen adherence Study or drug termination specifications Switches in medications

Continuity of Data Checks:

19 Continuity of Data Checks Refers to checking continuity of events that occur across study across visits Includes Adverse Events Medications Treatments/Procedures Overlapping Start/Stop Dates & Outcomes to be checked across visits

Continuity of Data Checks:

20 Continuity of Data Checks Overlapping dates across visits: Scenario : Per protocol, AE(s) are to be recorded on Visits 1, 3 & 5 The Adverse Event “Headache” is recorded on the AE form as follows: Visit Start Date Stop Date Outcome 1 01-Jan-2004 Continues 3 01-Jan-2004 12-Jan-2004 Resolved 5 12-Jan-2004 16-Jan-2004 Resolved

Continuity of Data Checks:

21 Continuity of Data Checks Visit Start Date Stop Date Outcome 1 01-Jan-2004 Continues 3 01-Jan-2004 Continues 5 01-Jan-2004 16-Jan-2004 Resolved Consistent reporting of Start and Stop dates of the continues of Adverse Event “Headache” across the three visits is as follows-

Consistency Checks:

22 Consistency Checks Designed to identify potential data errors by checking sequential order of dates corresponding events missing data (indicated as existing elsewhere) Involves cross checking between data points across CRF(s) within same CRF

Consistency Checks:

23 Consistency Checks Cross check across different CRF(s): AE is reported on the AE form with an action taken of “concomitant medication” Ensure corresponding concomitant medication is reported in the appropriate timeframe on the Concomitant Medication Record/Form. Event Start Date Stop Date Outcome Fever 13-Jun-2005 20-Jun-2005 Resolved Event Start Date Stop Date Outcome Paracetamol 14-Jun-2005 20-Jun-2005 Stopped

Consistency Checks:

24 Consistency Checks Cross check within same CRF: 1 st DCM: Report doses of antibiotics taken “before” intake of first dose of study drug 2 nd DCM: Report doses of antibiotics taken “after” intake of first dose of study drug: NOTE : First dose of study drug is taken on 15- May-2001 Antibiotic Dose Route Start Date Stop Date Amoxicillin 6 mg Oral 11-May-2001 14-May-2001 Antibiotic Dose Route Start Date Stop Date Streptomycin 7 mg IV 16-May-2001 17-May-2001

Coding Checks:

25 Coding Checks Textual or free text data collected & reported {AE(s), medication(s)} must be coded before they can be aggregated & used in summary analysis Coding consists of matching text collected on CRF to terms in a standard dictionary Items that cannot be matched, or coded without clarification from site Ulcers, for example, require a location (gastric, duodenal, mouth, foot, etc.) to be coded code

Range Checks:

26 Range Checks Designed to identify Statistical outliners Values that are physiologically impossible Values that are outside normal variation of population under study Ensure that appropriate range values are applied For e.g.., ranges for WBC can be applied either in ‘percentage‘ or in ‘absolute’ Ensure that appropriate ranges are applied depending on whether lab used is Primary Secondary

Range Checks:

27 Range Checks Cross check between Hematology record & AE record assuming that the Visit Date is 04 – Jan - 2006: Hematology Test Date Result Normal Range WBC 05-Jan-2006 13,710 cells/µL/cu mm 4,300 - 10,800 cells/µL/cu Event Start Date Stop Date Outcome Streptococcal infection 04-Jan-2006 07-Jan-2006 Resolved

External Data Checks:

28 External Data Checks Ensure receipt of all required external data from centralized vendors: Laboratory & PK Data Device Data (ECG, Vital Signs, Images) Missing e-data records to be tracked & requested from vendor on a periodic basis Missing data to be noted & corresponding values to be ‘re-loaded’ by vendor

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29 Examples of missing data/values: Missing collection time of blood sample Missing date of ECG Missing location of chest radiograph Missing systolic blood pressure Missing microbiological culture transmittal ID External Data Checks

External Data Checks:

30 External Data Checks Examples of invalid data/values: Incorrect loading of visit number Incorrect loading of subject number Incorrect loading of date/time of collection External Data Checks

Duplicate Data Checks:

31 Duplicate Data Checks Refers to duplicate entries within a single CRF across similar CRFs Duplicate entries & duplicate records to be deleted per guideline specifications Example: Treatment ‘physiotherapy’ on ‘30-Aug-2001’ reported twice on either same Treatment Record or across two different Treatment Records

Duplicate Data Checks:

32 Duplicate Data Checks Examples: Both Visit 4 & Visit 10 Blood Chemistry CRF(s) (with different collection dates) are updated with same values for all tests performed Both ‘primary’ & ‘additional’ Medical History CRF(s) at Screening are reported with same details of abnormalities Which one to retain…?

Textual Data Checks:

33 Textual Data Checks All textual data to be proofread & checked for spelling errors Obvious misspelled verbatim to be corrected per Internal Correction/clarify with the investigative site (as specified by guidelines) Common examples of textual data: Abnormalities/pre-existing conditions in Medical History record Adverse Events Medications/Antibiotics Project & study-specific data

Visit Sequence Checks:

34 Visit Sequence Checks Sequence of visits should be reviewed & if out of sequence, should be either queried corrected per Internal Correction (as per guidelines) Either a single CRF or a group of CRF(s) could be out of sequence with that particular visit

Visit Sequence Checks :

35 Visit Sequence Checks Visit Visit date 1 01-Jan-2000 2 02-Jan-2000 3 03-Jan-2000 4 04-Jan-2000 Visit VITAL SIGN Record Date of Vitals 1 01-Jan-2000 2 03-Jan-2000 3 02-Jan-2000 4 04-Jan-2000 Screening Record Visit date Demography 20-Feb-2006 Med. History 20-Feb- 2005 Inclusion Criteria 20-Feb-2006 AE 20-Feb-2006

SAE Reconciliation Checks:

36 SAE Reconciliation Checks All SAE(s) reported on CRF(s) checked by the Data Management should be reconciled with those reported on SAE Reports (with the sponsor) & vice versa Communication to be maintained with Sponsor Clinical Scientist

Documents to be Followed:

37 Documents to be Followed Protocol Guidelines – General & Project-Specific SOPs Subject Flowcharts Clean Patient Check Lists Tracking Spreadsheets

Slide 38:

38 Thank you!