1. Data Management in Clinical Research
Rosanne M. Pogash, MPA
Manager, PHS Data Management Unit
October 25, 2016

2. Data Management Topics
Data collection instruments
Common data elements/standardization
Data dictionary or codebook
Database structure
Data capture
Data entry
Data verification
Data quality
Data errors
Data error resolution
Audit trail
Data quality monitoring
Data integrity
Participant confidentiality/privacy
Database security
Data audits
External data transfers
Data sharing
Data preservation

3. Presentation Focus…….

4. Definition of Data Quality
Institute of Medicine
Data that are fit for use
Data that give the same result as error-free data
In the context of a research study
Data that accurately represent the measurements included in the statistical analysis plan

5. Definition of Data Quality
FDA- Clinical Trials Transformation Initiative (CTTI)
The ability to effectively and efficiently answer the intended question about the benefits and risks of a medical product (therapeutic or diagnostic) or procedure while assuring protection of human participants.

6. Team Approach to Research Design
Quality Research Data
Principal Investigator(s)
Content Experts
Statistician
Research Coordinator
Data Manager
IT Support
Regulatory Expert
Human Subjects Protection Expert

7. Public Health Sciences Contribution
Quality Research Data
Principal Investigator(s)
Content Experts
Statistician
Research Coordinator
Data Manager
IT Support
Regulatory Expert
Human Subjects Protection Expert

8. Team Contributions Statistical analysis Critical data
Critical processes
Data collection
Electronic interfaces
Database design
Data cleaning
Study monitoring
Regulatory compliance
Statistical analysis

10. Sources of Research Data
Face-to-face Interviews
Personal Devices
Scanners/Fax Machines
Forms/Surveys
Computer Assisted Telephone Interview (CATI)
Other Databases
Research Database
Physiological Measurements
Interactive Voice
Recognition (IVRS)
Audio/Video Recordings
Research Team
Medical Records

11. Improve Data Quality Prior to data collection
At the point of data entry
After data entry

12. Improve Data Quality Prior to data collection
At the point of data entry
After data entry

13. Prior to Data Collection
Define the data type for each variable
Categorical - defined response choices
Numeric
Date/Time
Text
Unique formats
Telephone number
Zip code

14. Prior to Data Collection
Minimize the number of variables allowing “free text” responses
Cannot be analyzed
Will need to be coded prior to analysis
Appropriate to describe “Other” if selected from a list of responses

15. Prior to Data Collection
Define the format
Numeric fields
Integer
Number with one decimal place
Number with two decimal places, etc.
Date fields
Month/Day/Year
Day/Month/Year
Year/Month/Day

16. Prior to Data Collection
Define the format
Time fields
12-hour clock with AM and PM designations
24-hour clock
HH:MM
MM:SS
HH:MM:SS

17. Prior to Data Collection
Define the units of a numeric value
pounds versus kilograms
inches versus centimeters
g/dL
mg/dL
U/L
mmHg

18. Prior to Data Collection
Code categorical variables
Ordinal – represents degree of measurement
1 = Mild
2 = Moderate
3 = Severe
1 = Strongly Disagree
2 = Disagree
3 = Agree
4 = Strongly Agree

19. Prior to Data Collection
Code categorical variables
Nominal - arbitrary; does not represent degree of measurement
0 = No
1 = Yes
9 = Don’t know
1 = Male
2 = Female
Be consistent throughout the study by using the same codes

20. Improve Data Quality Prior to data collection
At the point of data entry
After data entry

21. At the point of data entry
Choose a software application where you can minimize data entry errors by allowing you to:
Define the data type
Define the format
Control permissible values
Defined codes
Ranges for numeric variables

22. At the point of data entry
Microsoft Excel is an acceptable option ONLY if you use features which can minimize data entry errors
Cell formatting
Data validation
Freeze panes
Microsoft Excel lacks
Ability for multiple users to enter data at the same time
Adequate audit trail recording who and when edits are made
Access security
Role-based access

23. Excel Data Validation

24. Excel Data Validation

25. Excel Data Validation

26. Excel Cell Formatting

27. At the point of data entry
Research Electronic Data Capture application (REDCap)
Developed by CTSA-supported group at Vanderbilt University
Penn State has its own license
Maintained by HMC Research IT
Free of charge to use
Used by over 2060 institutions in107 countries

28. Build electronic data collection forms with variable validation, branching logic, calculated fields
Create a study database structure
Create and distribute surveys
Import electronic data from other sources
Export data to common data analysis packages
Review an audit trail of every action completed in the study database
Create and view reports
Store study related documents
Create and execute data quality rules
Resolve data errors across individuals
View a graphical representation of data
Control rights based on roles in study

30. Creating Forms in REDCap

31. Creating Forms in REDCap

32. Creating Forms in REDCap

33. Creating Forms in REDCap

34. Creating Forms in REDCap

35. Improve Data Quality Prior to data collection
At the point of data entry
After data entry

36. Verify that the data entry was completed accurately
After data entry
Verify that the data entry was completed accurately
Perform double data entry and compare the two entries for inconsistencies
REDCap has a double data entry option with a data comparison tool
Visually audit the electronic data by selecting a random sample of records

37. Create data quality rules to identify potential data errors
After data entry
Create data quality rules to identify potential data errors
Missing values
Out-of-range values
Problems with branching logic
Illogical/inconsistent data

38. After data entry
REDCap has 7 pre-defined data quality rules that you can execute following data entry.
Missing values (excluding missing values due to branching logic)
Missing values for required fields only
Incorrect data type
Out-of-range values
Outliers for numerical fields
Hidden fields that contain values
Multiple choice fields with invalid values
You can create customized rules as well.

40. Example of a Study Design in REDCap

41. Example of a Study Design in REDCap

42. PHS Data Management Services
Develop data management plans
Design data collection forms
Design administrative forms to facilitate data collection and protocol adherence
Provide design assistance to investigators/coordinators creating REDCap projects
Create REDCap projects – forms, surveys, and data quality rules
Create and implement testing plans for REDCap designs and data quality rules
Perform data entry
Perform study monitoring activities

43. PHS Data Management Services
Up to 10 hours of services may be free using funds supported by the CTSI
Contact:
Rosanne Pogash Manager, PHS Data Management Unit