1. Research Data Management with REDCap

2. Agenda What can I do with a database? Why use a database?
Research Data Management Best Practices
What are some software options and how do they compare?
What is REDCap?
Example of an existing REDCap
Guided Practice
On Own Practice
Review of On Own Practice

3. Databases-What can I do with them?
A collection of data stored in a way that makes them useful
Systematically organized
Store data
Many patients
Variables for individual patients over time
Organize data in a way that makes it easy to enter and easy to use
Query the database (retrieve data)
How many patients had an MI?
Extract datasets to analyze
Outcomes, exposures, confounders and effect modifiers

4. Why use a database? Allows for managing data
Entry, manipulation, export
Allows access for those who need it
Data entry
Data export for analysis
May offer needed security

5. Why use a database?
Allows data for multiple subjects to be collected in a single place
Using a database, you can find associations that you can’t find with just a few patients.
Data can be aggregated
Example: Do antibiotics cause MI?
We can’t tell from one observation
Of 1,000 patients who had an MI, 50 (5%) were on antibiotics within the prior year of their MI
Of 3,000 who did not have an MI, 160 (5.3%) were on an antibiotic
No association between MI and antibiotic (p=0.74)

6. Why use a database?
Database allows you to VALIDATE or set controls for what data can be entered and how it can be entered.
Spreadsheet:
Database
Study ID
Sex
Date of Birth
Age at enrollment 1 M
14/12/1980 30 2 F
11/26/1976 45 3
female
20/01/12
The first example is a spreadsheet. Notice how the Sex is sometimes just a letter, and sometimes written out. The date of birth sometimes has the day first, sometimes has the month first, and can have either 2 or 4 digit years. The age is a calculated field, that is incorrect in a spreadsheet, which defaults to the current century.
Study ID
Sex
Date of Birth
Age at enrollment 1 M
14/12/1980 30 2 F
26/11/1976 45 3
20/01/1912
103

7. Why use a database?
Databases maintain INTEGRITY of a record, whereas spreadsheets can be sorted independently by columns
Spreadsheet—Sorted by date of birth
Database—Sorted by date of birth
Study ID
Sex
Date of Birth
Age at enrollment 1 M
20/01/1912 30 2 F
26/11/1976 45 3
14/12/1980
In the top example a spreadsheet was sorted by the date of birth. Notice that only the date of birth was sorted, so the study ID, sex and age at enrollment no longer match up to the date of birth. In the bottom example, a database was sorted by date of birth. Notice that the database kept the study ID, sex and age at enrollment with the correct corresponding date of birth.
Study ID
Sex
Date of Birth
Age at enrollment 3 F
20/01/1912 2
26/11/1976 45 1 M
14/12/1980 30

8. Why use a database? Security Practicality Reliability
Can require authentication (login and password)
Can be encrypted
Can tell who accessed the data and what they did
Practicality
Multiple users can enter data at the same time
Allows large numbers of records (patients/visits) and columns (variables) compared to spreadsheeets
Reliability
Eliminates possibility of multiple different versions of data (happens frequently with spreadsheets)

9. Research Data Management Best Practices

10. Research Data Management Best Practices
Is there a specific question or hypothesis?
If no, STOP and determine the key question(s).
Identify Key Variables to answer question
Outcomes
Explanatory Variables/Exposures
Secondary Explanatory Variables
Confounders
Effect Modifiers

11. Research Data Management Best Practices—Define Variables
Set objective criteria whenever possible
For example:
High blood pressure is considered 3 consecutive blood pressure measurements with the systolic > X.
Determine the source of the data
Instrument, provider, patient, relative of patient

12. Research Data Management Best Practices—Determine Variable Types
Numeric (integer vs. decimal)
Categorical
Yes/No
Absence/Presence
Date (8JUL2016)
Time (13:42)
Datetime (8JUL :42)

13. Research Data Management Best Practices—Key Questions
Can the research question be answered with the variables included?
How will the data be analyzed?
What level of granularity is needed?
Exact values v. Groups of values
Will you need to derive analysis variables?
Calculate age, BMI
Is it acceptable or necessary to specify ‘unknown’?
Is there a difference between unknown and missing?
Are responses for a given variable mutually exclusive?
Check box, individual questions v. radio buttons/dropdown
Unknown, Missing and No example:
No value for date of birth: does this mean that the person has no date of birth or that this data was not collected?
Do not allow a missing value to equal no (no tobacco use is different than not asking someone if they use tobacco)

14. Research Data Management Best Practices—Metadata
Always document!
Record ALL the information for the decisions you make
Ensure that this is ACCESSIBLE and READABLE for those entering, managing analyzing the data

15. Research Data Management Best Practices—Form Creation
Consistency
Questions using the same answer choices should use same order of answers
Provide hints, clarifications and definitions
Phrase questions in the positive
Yes: Did the patient complete the visit rather
No: Did the patient not complete the visit
Avoid check boxes when possible
Difficult to tell if not checked because missed v. not applicable

16. Types of applications commonly used as database
Requires a license ($$)
Microsoft Excel
Microsoft Access
SQL Server
Oracle
Open Source/Free
REDCap
EpiInfo
Open Office Calc
Postgres
MySQL

17. Selecting a database Consider the tasks you will need to perform
Data collection and editing?
Data transformation/calculations?
Basic vs. advanced statistics?
Making figures?
A single platform may not be enough, but use as few as possible
Software
Mac PC
Data entry/
Forms
Data editing
Transform data
Basic stats
Adv statics
Figures
REDCap
web
+++ - +
Epi Info X ++
Open Office
R/RStudio

18. Benefits of some applications
REDCap
Web-based point-and-click interface
Requires little training
HIPAA compliant (secure)
EpiInfo
Easy to set-up, well known in stats community
Can use for data storage AND analysis
Excel/Open Office Calc
Easy set-up
Postgres
Can have multiple related tables
Set acceptable formats within a column
Powerful (many data points)

19. Potential Problems with some applications
Excel/Open Office Calc
Uncertain data integrity
Limited ability to validate data
Problematic sorting (e.g. might only sort a single column)
Limited in number of rows and columns
Postgres
No data entry interface – must create elsewhere
Requires SQL training to manipulate and extract data
EpiInfo
Limited data validation functionality
No longitudinal mode, must create multiple variables for each time point
REDCap
Requires IT support to set-up and maintain
Need internet connection, at least for syncing

20. REDCap
A database with data entry forms that is supported by the University of Zimbabwe
Secure platform for data storage (protects patient data)
Accessible via the internet:
You can make your own database!

21. REDCap Features Develop forms through web interface or .csv file
Data formatting (dates v. numbers v. integers v. check boxes v. radio buttons/drop downs)
Hint fields
Data validation checks
Branching logic (controls field availability)
Import data from other sources
Export data to multiple formats (Excel, R, Stata…)
De-identification features
Some built-in reporting and visualization tools
Store documents (PDF, jpeg, doc)
User and group-level security
Demo of each feature to follow

22. Guided Practice
Create a new project called ‘my first database’

23. Guided Practice
Create a ‘demographics’ form

24. Guided Practice Create a ‘demographics’ form
First, press ‘create’, then the green box at the bottom will appear, where you can enter the name of the form. Once you enter the name, press ‘create’. After it is created, click on the form name, so that you can open it up and modify it.

25. Guided Practice Add the following field: Patient First Name
Text Box (short text)
Required
Identifier

26. Guided Practice Create the field: Study ID
You should always start the first form with a common identifier that will be used to link multiple forms

27. Guided Practice Add the following field: Patient First Name
Text Box (short text)
Required
Identifier

28. Guided Practice Repeat Steps for Patient Last Name
Text Box (short text)
Required
Identifier

29. Guided Practice Add the following field: Birthdate
Text box (short text)
Date format ‘D-M-Y’
Required
Identifier

30. Guided Practice Add the following field: Sex
Use drop down for the options: Male, Female, Unknown
Make this field required
Note that you do not have to assign the numeric value, REDCap will assign this for you. If you want to use different values, you can enter them yourself.

31. Guided Practice
Create a new form called ‘Baseline Visit’

32. Guided Practice Add the following field Date of visit
Date format ‘D-M-Y’
Required
Identifier

33. Guided Practice
Create a section before Date of Visit called ‘Visit Information’

34. Guided Practice Add the following field: Were vitals obtained?
Yes/no field
Required

35. Guided Practice Add the following field: Heart Rate Format: integer
Minimum: 0
Maximum: 500

36. Guided Practice Add the following field: Temperature
Format: one decimal place
Hint text: Celsius
Minimum: 0
Maximum: 45

37. Guided Practice
Add a section between Date of Visit and Heart Rate and call it ‘Vital Signs’

38. Guided Practice
This is what the form now looks like in online designer

39. Guided Practice Add branching logic
Click on the double green arrows in the Heart Rate field.
In the box that appears, go to the bottom section, called Drag-N-Drop Logic Builder, click on the field/value set: vitals_obtained = Yes (1), and drag this to the next column

40. Guided Practice
Add branching logic with advanced syntax to the temperature field
This time, instead of using the ‘drag and drop’ logic builder, use the Advanced logic syntax. Type [vitals_obtained] = ‘1’

41. Guided Practice
Here is what the form now looks like in online designer. Notice the text that indicates branching logic exists.

42. Guided Practice
Here is what it looks like if you use the preview in Online Designer

43. Guided Practice: Data Entry
Here is what the form looks like when you open it to enter data

44. Guided Practice
Once you answer the vitals obtained question, the heart rate and temperature become visible.

45. Guided Practice-Graphical Data View

46. Guided Practice-Graphical Data View

47. Guided Practice-Graphical Data View
Since Patient Last Name is free-text, the graphical view only tells you how many records there are and how many are missing.
Since the sex variable is a drop down, you can see a bar chart of the values, in addition to how many unique values are in the data (in this case, only 2 of the 3)

48. Guided Practice-Graphical Data View
If we look at the other form, we see additional types of data. Here, you can see descriptive statistics for a continuous variable (heart rate), with a plot of the points. The median value is in red. Note that it indicates that one value is missing, which is 20% of the data. This is important to note when you are analyzing data, and you may need to go back and collect this missing value, if it is still available.

49. Guided Practice-Graphical Data View

50. Guided Practice-Exporting Data
Click on ‘Data export Tool’
You can either choose the simple export (all data) or an advanced export, where you choose the fields

51. Guided Practice-Exporting data
Here are all the formats you can export the data in. Click on the file type(s) that you prefer in the right-hand column

52. Guided Practice-Importing a Data Dictionary
First, export the existing data dictionary. Do this by going to Project Setup and then clicking on the ‘Download current data dictionary’ hypertext.

53. Guided Practice-Importing a Data Dictionary
Open the .csv file (opens in Excel or Open Office Calc).
Enter two new fields:
Sbp (systolic blood pressure):
Form=baseline_visit
Field Type = text
Field Label=Systolic Blood Pressure
Field Note=mmhg
Text Validation=integer
Text Validation min = 10
Text Validation max = 250
Branching Logic=[vitals_obtained]=‘1’
Create second variable dbp (diastolic blood pressure), with all the same information, except name and label.
Save the file.

54. Guided Practice-Importing a Data Dictionary
Next, click on ’upload data dictionary’.

55. Guided Practice-Importing a Data Dictionary
Now, import the /csv file you just modified. Click browse and find the file you just modified. Then press ‘upload file’

56. Guided Practice-Importing a Data Dictionary
You will be asked to commit changes from the import.

57. Guided Practice-Importing a Data Dictionary
You will be told that the import was successful. If you do not receive this message, then you will need to troubleshoot what is wrong.

58. Guided Practice-Importing a Data Dictionary
Here is what the form looks like now in online designer.