1. Database Management for Clinical Research Tables, Normalization, Queries, and Forms Michael A. Kohn, MD, MPP 3 September 2013

2. Clinical Research* Choose the study design, and define the study population, predictor variables, and outcome variables; measure these variables and anticipate problems with measurement; analyze the results Today, we discuss the “ nitty gritty ” of collecting, storing, updating, and monitoring the study measurements. *Private companies that make data management systems for clinical research understand “ clinical research ” to include only trials preparatory to FDA drug or device approval, not observational studies.

3. Outline Online Surveys Data Tables Rows = Records; Columns = Fields Data Dictionary (Metadata) On-screen forms ( “ EDC ”, “ eCRFs ” ) Normalization of Data Tables Database Queries Data management programs (platforms)

4. “All-that-apply” Questions

6. Single Best Response

7. Skip Logic

9. DCR-4 Website

10. Online Survey Tools REDCap Qualtrics SurveyMonkey Zoomerang, Google Forms, etc.

11. Components of Data Management 1) Collecting/entering 2) Monitoring/checking/cleaning 3) Reporting 4) Statistical analysis 5) Maintaining confidentiality 6) Back-ups

12. Default Option Collect data by hand-writing on paper forms Transcribe into Excel tables Import into Stata for monitoring, reporting and analysis

13. Rows = Records = Entities Columns = Fields = Attributes Data Tables

14. DCR Chapter 16 Exercise 2 The PHTSE (Pre-Hospital Treatment of Status Epilepticus) Study was a randomized blinded trial of lorazepam, diazepam, or placebo in the treatment of pre-hospital status epilepticus. The primary endpoint was termination of convulsions by hospital arrival. To enroll patients, paramedics contacted base hospital physicians by radio. The following are base-hospital physician data collection forms for 2 enrolled patients: Lowenstein DH, Alldredge BK, Allen F, Neuhaus J, Corry M, Gottwald M, et al. The prehospital treatment of status epilepticus (PHTSE) study: design and methodology. Control Clin Trials 2001;22(3):290-309. Alldredge BK, Gelb AM, Isaacs SM, Corry MD, Allen F, Ulrich S, et al. A comparison of lorazepam, diazepam, and placebo for the treatment of out-of-hospital status epilepticus. N Engl J Med 2001;345(9):631-7.

16. Demonstration: Creating a Data Table Enter a row of data in datasheet view (PHTSE table) Where is predictor on data collection form?

17. Demonstration: Data Dictionary Table design view: field (=column) names, data types, definitions, validation rules

18. Create a 9-field data dictionary for the data table

19. Demonstration: Same Table in Excel, Stata Excel Stata Etc Rows = Records = Entities Columns = Fields = Attributes Access and Stata have a special row at the top for column headings (=field names); Excel just uses the first row.

20. (Use EnollmentWithUnblindedGroupAssignments query)

21. On-screen Forms EDC = Electronic Data Capture eCRF = Electronic Case Report Form

22. Advantages of On-Screen Forms Data keyed directly into the computer data tables without a transcription step Include validation checks and provide immediate feedback when a response is out of range Incorporate skip logic

24. Demonstrate PHTSE Enrollment Form

25. Normalization

26. Table of Study Subjects Row = Enrollment Columns = ID#, KitNumber, Date/Time, HospArrSzAct Research nurse collects several variables for each of the patient’s hospital days: Intubation, Lowest O2 Saturation, Tmax, anti-seizure medications and doses, … Adverse event tracking Call logs Table of PHTSE Enrollments

27. If study subjects have an indeterminate number of hospital days, and each hospital day is associated with an indeterminate number of medications, “ normalize ” the records into 3 tables, one for subjects, one for hospital days, and one for medications. Keep adverse events in a table with one row per event and calls in another table with row per call. Normalization For more on normalization, take Epi 218.

28. Database Queries* SQL = Structured Query Language * Not to be confused with queries to study sites, author queries, etc.

29. Select Queries Select queries (aka “ Views ” ) organize, sort, filter, and display data. Queries use Standard Query Language (SQL), but you don ’ t have to learn it, because of graphical query design tools. A query can join data from two or more tables, display only selected fields, and filter for records that meet certain criteria.

30. Platforms for Epi 218 Access (Labs 1, 2 and 3) REDCap (Lab 4, 5) QuesGen (Lab 6) May use other data platforms for final project: -- Oncore -- Filemaker Pro -- OpenClinica -- Other Note that the list does NOT include Excel.

31. Microsoft Access Integrated desktop database management platform Uses SQL (Structured Query Language) Has an outstanding graphical query design tool Incorporates an excellent report writer Based on the principles of the Relational Model Relationships diagram has integrated referential integrity Very flexible, infinitely customizable NOT browser based (desktop application) Using advanced features usually requires hiring a developer

32. Microsoft Access UCSF MyResearch DEB Terminal Server

33. Provides a remote Windows desktop with Microsoft Office Professional Remote Desktop client software freely available for the Mac and already part of Windows Obtain DEB Terminal Server username and password from HelpDesk@psg.ucsf.eduHelpDesk@psg.ucsf.edu Instructions available on Epi 218 course syllabus page

34. REDCap Web-based research data collection system developed at Vanderbilt Available free through UCSF Academic Research Systems http://tinyurl.com/yh5m6ka You are both the Principal Investigator and User 1. Model= “ Do-it-yourself ”

35. QuesGen Web-enabled research data collection and management platform developed (with UCSF input) by a private company based in Burlingame More full-featured and customizable than REDCap, but primarily “ pay-us-to- do-it ” rather than “ do-it-yourself ”

36. Transcribing into Excel tables No data dictionary (meta-data) No automatic data validation No on-screen forms No skip logic Significant security and file corruption issues Not acceptable for Epi 218 Final Project.

37. General description of database Data collection and entry Error checking and data validation Analysis (e.g., export to Stata) Security/confidentiality Back up Elements of a Data Management Plan

38. Final Project 1) What is your study? ("The [CUTE ACRONYM] study is a [DESIGN] study of the association between [PREDICTOR] and [OUTCOME] in [STUDY POPULATION]"). 2) What data points are you collecting? (Your data collection forms.) 3) Who collects the data? You? RAs? MDs? Chart abstractors? Maybe the study subjects enter the data themselves?

39. Final Project (cont’d) 4) How are the data collected? Written onto a paper form and then transcribed into a computer database? Entered directly into the computer? (If data are transcribed, who does it? Have you hired/will you hire somebody? Or have you enlisted/will you enlist some med students?) 5) Will the above-mentioned computer database be in Access, REDCap, QuesGen, Oncore, OpenClinica, SurveyMonkey, Medidate RAVE, or something else? 6) Try to provide a detailed data dictionary with the name, data type, description, and validation rules for each field (column) in the data table(s).

40. Final Project 7) If it's a multi-table database, even a hand-drawn relationships diagram would help but is not required. 8) How do you validate the data for correctness and monitor the data collection effort? (Usually you have some range checks on individual variables and you periodically query for outliers that are nonetheless within the allowed range.) 9) You should periodically analyze the data, not only to look for problems, but also to see where the study is headed. How do/will you do this? Query in Access and export to Stata? 10) How will you protect your subjects' identifying data? 11) How will you ensure that you don't lose your data file in a computer crash or if a water pipe leaks?

41. Answering these questions is an essential part of doing a clinical research study.