Managing Clinical Data Using REDCap

1. Managing Clinical Data Using REDCap  Peter E. Gabriel, MD Andrew J Cucchiara, PhD October 11, 2012

2. The Research Database Problem • The 27-version Excel spreadsheet… • The Access database created by the summer intern… • Paper surveys… • Hard drive crashes… • The statistician’s blues… • Sharing data outside Penn… • Security? Privacy? Audit trails? Oh my!

3. The REDCap Solution • Research Electronic Data Capture • Web-based, user-friendly database system, originally developed at Vanderbilt University • Now overseen by the REDCap Consortium: 473 institutional partners in 48 countries • Supports concurrent access by multiple users from anywhere via web browser • Robust data integrity, nightly backups, etc. • Excellent security and privacy features, with extensive audit logging • HIPAA-compliant, 21 CFR Part 11 capable • Easy to export data to Excel and statistical packages • Supports surveys, ad-hoc reporting, event scheduling, file sharing, auto- data validation, branching logic, calculated fields, and more

4. Accessing REDCap UPHS: https://redcap.med.upenn.edu CHOP: https://redcap.research.chop.edu

5. Agenda • Creating and Managing Projects • Building Data Collection Forms • Entering Data • Controlling User Access Rights • Ad Hoc Reports and Exporting Data • (Advanced Tools)

6. Creating a New Project Enter Project Title:

7. Select a Project Purpose Only used to track usage statistics – does not affect functionality

8. Select a Project Type & Collection Format • Key Question: Is REDCap right for your project?

9. REDCap Project Types • Single Survey • Ideal for collecting anonymous, one-time responses from participants – similar to a basic SurveyMonkey survey • Participants are emailed a link that points to a web form in order to collect responses; they do not need to have a REDCap account • Data Entry Forms • Intended for data capture by clinicians with a REDCap account • Single Survey + Data Entry Forms • Can be used to initially populate records with participant responses in order to initiate data collection (example: pre-screening survey)

10. Data Entry Collection Format • Classic Data Collection • Data to be collected once per subject – i.e. one “record” per subject • Longitudinal/Repeating Collection • Data collected multiple times per subject • Fixed number of collection points that correspond to pre-defined events, e.g. Initial Evaluation, 3mo. follow-up, 6mo. follow-up, 1yr follow-up • Optional scheduling via project calendar

11. A Brief Word on Data Relationships • One-to-many and many-to-many relationships are common in healthcare data • E.g. one patient can have many diagnoses, procedures, medications, lab results, etc. • Data sets containing these complex relationships may need to be restructured in order to work with REDCap • Example: This will not work in REDCap as structured.

12. Options for Restructuring Data • Convert multi-valued data fields into a series of “yes/no” fields • Example: Aspirin Yes/No? Beta blocker Yes/No? • Summarize your longitudinal data points into aggregate statistics over a fixed time period and use “Classic” collection format • Example: Min Hgb A1c, Max Hgb A1c, etc. • Align your longitudinal data points with pre-defined events and use “Longitudinal” collection format • Example: Pre-Treatment PSA, 6-month post-treatment PSA

13. Restructuring Data - Examples • Complex data with many-to-many relationships… • …restructured to be “flat:”

14. Restructuring Data - Examples • Complex data with many-to-many relationships… • …restructured to be “event-based:”

15. Creating a New Project Enter Project Title:

16. Project Setup Navigation Pane Center Work Area

17. Project Setup These buttons are only a “to-do list” for your benefit – they do not control project functionality

18. Project Setup We just did this when we created the project – click here if you need to go back and change the format again

19. Project Setup Advanced settings – more on these later

20. Project Setup Copying, archiving, and deleting a project

21. Project Setup Best way to get started building forms

22. Data Collection Instruments • An instrument is a single data entry form • A subject has exactly one record across all data entry forms – i.e. all the data fields eventually combine into one big row per subject in the exported data

23. Data Collection Instruments (cont.) • So why have more than one instrument? • Logical grouping of related data fields • Can control user access at the instrument level – e.g. a data entry assistant can be restricted from seeing the demographics form that contains PHI

24. REDCap Shared Library • The REDCap Shared Library can be a good source for standardized instruments, e.g. CDASH, SF-36, FACT-G, etc. • Content will hopefully grow over time

25. Data Collection Instruments • Click instrument name link to: • Modify an existing form • Add additional fields to form • Modify existing questions • Change attributes of questions

26. Online Designer • Data Fields • Add New Field • Section Header • Preview Button

27. Data Field Operations

28. A Note on the Study ID Field • The first data field in every project is the unique record identifier for that project • Default variable name is “study_id” but you can rename it to something else • Can be a “real” ID like MRN or SSN, or a randomly-assigned number. Must be unique. • Can set it to be auto-assigned by REDCap (Project Setup  Make customizations  Use auto-numbering for naming new project records)

29. Creating / Editing a Data Field

30. Field Types

31. Defining the Field Attributes • Variable Name • Unique data column name • Validation • Data type/format constraints • Required • Mandatory field • Identifier • Mark as an identifier (PHI) • Custom Alignment • Question arrangement • Field Note • Additional instruction for data entry person

32. Variable Name Requirements • Should be descriptive (i.e. not cryptic as with a1, xyz, lol). The variable name is how analysis data is referenced. • The first character of a variable name must be an alphabetical character (i.e., A to Z or a to z). • All other characters of a variable name may contain alphabetical characters, numbers 0 to 9 or underscores (i.e., spaces, punctuation marks, mathematical functions, special characters and symbols are NOT allowed). • Length of variable names should contain fewer than 26 characters; shorter variable lengths are better to reduce the risk of truncation by statistical analysis packages. • Variable names must be unique among all instruments (i.e. forms) within a specific REDCap Project.

33. Text Field Validation • Verifies data input to prevent invalid entry, prior to form submission

34. Required Fields • Required fields are labeled with *must provide value • Warning prompt when trying to save: Not a “hard stop” - possible to override

35. “Identifier” Fields • Fields that constitute protected health information (PHI) can be marked as an “Identifier.” • These fields can then be excluded on data export, allowing for analysis of “de-identified” data • Users can also be restricted in their ability to export Identifier fields based on access rights

36. Custom Alignment Examples • Custom Alignment controls the position and orientation of the responses on data entry forms.

37. Permissible Values for Multiple Choice Fields • A value (1 to n) is automatically assigned to each choice when the field is saved: • Can also manually code values yourself by entering ‘# , text description’

38. Creating a Conditional Field • Branching logic can be used to show fields that meet a certain condition

39. Creating a Conditional Field (cont.) • Specify the variable and value of the field that makes the condition true • Complex AND / OR / NOT logic is possible is possible with the “Advanced Branching Logic Syntax” (vs. the “Drag-N-Drop Logic Builder”)

40. Creating a Conditional Field (cont.) • The field containing branching logic will show/hide based on the value of the field(s) it depends on

41. Agenda • Creating and Managing Projects • Building Data Collection Forms • Entering Data • Controlling User Access Rights • Ad Hoc Reports and Exporting Data • (Advanced Tools)

42. Starting Data Collection • The Data Collection area of the navigation pane, lists all the forms (aka instruments) available for the project • In order to create a new record, the form that contains the Study ID must be entered first.

43. Creating a New Study ID • A new record is created whenever a non-existing identifier is entered on the first page of the data collection interface. • If the record already exists then the record for that Study ID is retrieved. 1234567

44. Using an Existing Study ID • An existing Study ID can also be selected from the appropriate dropdown list on the first page of the data collection interface.

45. Form Status • All collection forms (aka instruments) have a “Form Status” field at the end of the form • There are three possible record statuses: • Incomplete • Unverified • Complete • “Save Record” saves and exits the record • “Save and Continue” saves and opens the next collection form if there is one, or saves and keeps the current record open if not

46. Viewing the Edit History • To view the edit history for a particular field, click the “H” icon next to the field:

47. User Rights and Permissions • Allows you to grant a user full or partial access to the project

48. Granting User Privileges • Basic user rights include access to various modules and ability to export PHI

49. Granting User Privileges • Ability to lock a particular record from further editing • Ability to Create / Rename / Delete project records • Data Entry Rights are specified individually for each data collection form

50. Data Access Groups • Data Access Groups is an advanced feature useful for multi-center trials and collaborations • Users in a particular Data Access Group can only see records entered by other users in that Data Access Group