1. EPI 218 Database Management for Clinical Research Michael A. Kohn, MD, MPP January 8, 2008

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 In this course, 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 RCTs preparatory to FDA drug or device approval, not observational studies.

3. Outline Housekeeping Data Tables –Rows = Records; Columns = Fields Normalization of Data Tables Queries Front End or Interface/On Screen Forms

4. Assumptions about Students Actively involved in a clinical research study Some experience with entering and maintaining data in single-table spreadsheet or statistical software Some of you are here mainly to learn how to query an existing database

5. Course website: http://www.epibiostat.ucsf.edu/courses/schedule/data_management.html Labs will be in China Basin Landing 6704 with overflow into 6702, 8:45 – 10:15 http://apps.epi-ucsf.org (For log-on: smead@psg.ucsf.edu) Citrix Metaframe Presentation Server  MS Office Desktop “Learn MS Access 2000” CD

6. Lab Instructors Sarath Raju Foloshade Jose

7. Course Objectives Learn how to develop a multi-table, relational database for a research study. We will be using Microsoft Access, but we are familiar with other database software. Learn how to query a database for monitoring and analyzing data in a research study. Example: Infant Jaundice Study

8. Requirements Turn in all 4 assignments on time Fill out course evaluation.

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

10. 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.

13. Display the data from these 2 data collection forms in a 2-row data table. Subjec tID KitNum ber AdminDat e Admin Time SzStopPre Hosp SzStopPreHos pTime HospArrT ime HospArrS zAct HospArrG CSV 189A3223/12/199417:39FALSE 17:48TRUE 410B53612/1/199801:35TRUE01:3901:53FALSE4

14. Create a 9-field data dictionary for the data table Field Name Data TypeDescriptionValidation Rule SubjectIDIntegerUnique Subject Identifier KitNumberText(5)5-character Investigational Pharmacy Code AdminDateDateDate Study Drug Administered AdminTimeTimeTime Study Drug Administered SzStopPreHospYes/NoDid seizure stop during pre- hospital course? SzStopPreHosp Time TimeTime seizures stopped during pre-hosp course (blank if seizure did not stop) HospArrTimeTimeHospital Arrival Time HospArrSzActYes/NoWas there continued Seizure Activity on Hospital Arrival? Check against SzStopPreHosp HospArrGCSVIntegerVerbal GCS on Hospital Arrival (blank if seizure continued) Between 1 and 5

15. Methods: Design-Nested double cohort study. Setting-Kaiser Subjects-Infants with neonatal jaundice and randomly selected non-jaundiced infants Predictor Variable-Presence or absence of jaundice Outcome Variable- Neuropsychological score (ranging from 55 to 145) at age 5 Analysis- ? JIFee Jaundice and Infant Feeding Study Newman, T. B., P. Liljestrand, et al. (2006). "Outcomes among newborns with total serum bilirubin levels of 25 mg per deciliter or more." N Engl J Med 354(18): 1889-900.

16. Infant Jaundice Study Data 1.Approximately 400 children 2.5 examiners (doctors) 3.Approximately 700 neuropsychiatric examinations, measuring weight, height, and “NPScore” (IQ) 4.Some children to be examined more than once 5.No examiner to see the same child twice 6.If child died before age 5, store age and circumstances of death

17. Infant Jaundice Study Table of Subjects = “Baby” Row = Individual Infant Columns = ID#, Name, DOB, Sex, Jaundice. If one set of measurements per subject, put measurements in subject table. This is a single-table database. Table of Study Subjects

19. Demonstration: Creating a Data Table Label columns and enter rows of data in datasheet view Where is predictor on data collection form?

20. Demonstration: Data Dictionary Table design view: field (=column) names, data types, definitions, validation rules (More on data types, free-text vs. coded responses, later)

22. Acceptable table showing one set of exam results per participant. (BabyExamForFigure3)

23. Demonstration Disallowed values Duplicate primary keys This automatic error checking and data validation IS why you need to enter your data into a computer; it is NOT why you need a relational DBMS. Many single- table products (Filemaker Pro, SAS FSP, even Excel) can do error checking and data validation.

24. 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.

25. Normalization

26. Table of Study Subjects Row = Individual Infant Columns = ID#, Name, DOB, Sex, Jaundice If some infants have more than one exam, what do you do? Table of Study Subjects

27. Undesirable table showing multiple exam results per study participant. (BabyExamForFigure4)

28. Demo Find highest IQ Score Find all exams done in April

29. Common Error If you find yourself creating multiple columns for the same measurement, e.g., Date1, Score1, Date2, Score2, Date3, Score3, … Or if your table is more than about 30 columns wide, –It is time to restructure your table.

30. Undesirable table with participant-specific data duplicated for each exam. (Note problem with Helen’s DOB.) (ExamBabyForFigure5)

31. Demo Find highest IQ Score Find all exams in a particular month What is Helen’s birth date? What happened to Alejandro, Ryan, Zachary, and Jackson?

32. If some infants have multiple exams, “normalize” the records into two tables, one for subjects and one for examinations. Normalization

33. Data normalized into two tables: one (“Baby”) with rows comprising subject- specific information; the other (“Exam”) with rows comprising exam-specific information. Note that Helen can only have one birth date. Subjects with no exams, e.g. Alejandro, still appear in the database. “SubjectID” functions as the primary key in the “Baby” table and as the foreign key in the “Exam” table.

34. Figure 7. Relationships diagram showing the one-to-many relationship between the table of subjects (“Baby”) and the table of measurements (“Exam”).

35. Demonstration Inability to create integrity violations with normalized tables. This IS why you need a multi-table relational DBMS.

36. Undesirability of Storing Calculated Values Store raw data, not calculated fields, e.g., store dates and times; calculate intervals. Storing a patient’s birth date allows calculation of his or her exact age on the date of a particular measurement.

37. Figure 15. Storing calculated fields such as “AgeInMonths” is undesirable. What if the birth date for SubjectID 2322 (Helen) is corrected in the “Baby” table?

38. Queries

39. 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.

40. Demonstration Age in months and BMI at exam of subjects who were examined in January and February of 2010.

41. Select Queries Produce “Table-Like” Results Note that the result of a select query that joins two tables, displays only certain fields, selects rows based on special criteria, and calculates age and BMI still looks like a table in datasheet view. But, remember that it is merely a “view” of data from the underlying tables.

42. “Action Queries” Change Data 1)Update Query -- changes the values of specific fields in existing records 2)Append Query -- adds new records (rows) to a table 3)Delete Query -- deletes records from a table

43. Front End or Interface On-screen forms

44. 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

45. Standard Data Entry Conventions Several conventions for data entry and display have developed over time. Although most users of screen forms are not aware of these conventions, they have come to expect them subconsciously. For example, a series of mutually exclusive, collectively exhaustive choices is usually displayed as an “option group” consisting of several different “radio buttons”, whereas choices which are not mutually exclusive are displayed as check boxes. N.B. An “option group” of mutually exclusive choices is a single column or field. A group of N check boxes represents N yes/no fields.

46. Use check boxes when options are not mutually exclusive. (5 fields) Use radio buttons when options are mutually exclusive. (1 field) Computer chart abstraction form showing two common data entry conventions.

47. Demonstration Option group for examiner’s medical specialty MasterRaceAsFieldList, MasterRaceAsOptionGroup, MasterRaceAsAllThatApply

48. On-screen vs. paper forms Minimize the extent to which study measurements are recorded on paper forms. Enter data directly into the computer database or move data from paper forms into the computer database as close to the data collection time as possible. When you define a variable in a computer database, you specify both its format and its domain or range of allowed values. Using these format and domain specifications, computer data entry forms give immediate feedback about improper formats and values that are out of range. The best time to receive this feedback is when the study subject is still on site. Can only monitor data for outliers, systematic differences between data collectors or study sites, and study progress (I.e., query the data) once the data are in the computer. You can always print out a paper copy of the screen form or a report of the exam/interview results once the data are collected.