Data Management for Research Michael A. Kohn, MD, MPP January 4, 2005

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 3 groups: ATCR/MCR, EPI non-ATCR/MCR, and CRC (GCRC or PCRC).

Housekeeping Better of the 2 course websites: Check Lab Sheet to confirm/sign up for a lab session. (Sign up for the Tuesday 8:15 and 9:15 labs is restricted to ATCR/MCR students.) Labs will be in S165A. Bring a diskette, zip disk, or USB “memory stick,” and your syllabus to labs. (No printing in labs.) Syllabus and “Learn MS Access 2000” CD

Lab Instructors Kari Mazurek (Course Administrator) Mike Jarrett Andrew High Mandana Khalili (You will find that interacting with the lab instructors, during labs, outside of labs, and via , to be the most valuable part of this course.)

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

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

Assignments Lab 1: Tables and Relationships 01/11 or 01/12 Send Access file LastnameLab1.mdb to by 1/17 at 5 pm. We will work through these assignments in the labs, so you don’t need to have Access2000 at home. Lab 2: Queries, Reports, Importing Data 01/18 or 01/19 Save Access file as LastnameLab2.mdb Send to by 1/24 at 5 pm.

Assignments (cont’d) Optional (Required for ATCR.MCR): Write a sentence or two for the “Methods” section on inter- rater reliability. (Use Bland and Altman, BMJ 1996; 313:744) Lab 3: Querying, Exporting, and Analyzing Data Option A (Required for ATCR/MCR): Determine if neonatal jaundice was associated with the 5-year neuropsychiatric scores and create a table, figure, or paragraph appropriate for the “Results” section of a manuscript summarizing the association. Send assignment to by 1/31 at 5 pm.

Assignments (cont’d) Lab 3: Querying, Exporting, and Analyzing Data 01/25 or 01/26 Option B (not for ATCR/MCR students): Answer a research question of your own by querying an existing database. Display your results in a paragraph, table, or figure appropriate for presentation to others in your field. Send assignment to by 1/31 at 5

Assignments (cont’d) Class session 5 (not a lab): Planning and Budgeting for Data Management Option A: Write a one-page data management section for your research study protocol and create a budget for data management. (Please include an Access file for your database, if you have one. Also include a one- sentence summary of your study.) Send assignment to by 2/14 at 5

Assignments (cont’d) Class session 5 (not a lab): Planning and Budgeting for Data Management Option B: Write a one-page description with a relationships diagram for the database with which you currently work. Send assignment to by 2/14 at 5

Data Management for Clinical Research We know how to define the study population, the independent variables and the outcome variables; measure these variables and anticipate problems with measurement; analyze the results.

The DBMS (Database Management System) is for entering and storing the measurements, entering and storing the other information necessary to administer the study (subject contact information, exam schedules, reimbursement records, etc.), monitoring the study, and either analyzing the results or formatting the results for analysis. Data Management for Clinical Research

Four Types of Research Database 1.Combination of paper files, Excel spreadsheets, and direct keyboard entry into the statistical analysis package. 2.Desktop multi-table relational database. 3.Client-Server multi-table relational database. 4.Internet database server.

Ease of data entry Automatic data validation Automatic error checking Alternative is a stack of paper forms Advantages of a computerized database

Advantages of a Multi-Table Relational Database Eliminates redundancy Ensures data integrity Note: Unless you plan on doing your analysis long-hand, you always need a computer database of some sort (a Stata dataset or an Excel spreadsheet may be adequate); you don’t always need a multi-table relational DBMS (like Microsoft Access).

Collection of spreadsheet-like, two- dimensional tables. Rows in Tables = Records Columns in Tables = Attributes Tables are related one-to-many, many-to-many, and one-to-one. Multi-Table Relational Database

Jaundice and Infant Feeding Study Cohort study to determine the 5-year neuropsychiatric sequelae of infants with neonatal jaundice or feeding disorders.

Methods: Design-Cohort study. Setting-Single, urban medical center Subjects-Infants with neonatal jaundice and randomly selected non-jaundiced infants Predictor Variable-Presence or absence of jaundice Outcome Variable- Neuropsychiatric score (ranging from 55 to 145) at age 5 Analysis- ? Infant Jaundice Study (Our fictional version of JIFee)

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

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

Demonstration: Creating a Data Table Label columns and enter rows of data in datasheet view

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

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.

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

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.

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

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

Demo Find highest IQ Score Find all exams done in April

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.

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

Demo Find highest IQ Score Find all exams in a particular month What happened to Alejandro, Ryan, Zachary, and Jackson?

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

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.

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

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

Analogy to Double Data Entry Having different examiners see the same 5-year- old to establish the interrater reliability of the IQ score is analogous to doing double data entry. The same table structures and relationships would exist if the objective were to check data entry off of paper forms. When entering data directly into on-screen forms (with their automatic range checks and validation routines), double data entry may not be necessary.

Table of Examiners Neuropsychiatric outcomes are assessed by 5 different examiners (doctors) May want to assess whether examiner characteristics (sex, specialty, age) affect neuropsychiatric scores Doctor examines many children; each child may have more than one exam; but a child is never examined by the same doctor twice.

Table of examiners with multiple examiner-specific fields.

Figure 9. Undesirable table in which examiner-specific data is repeated with each examination. (Note that Dr. Novello is a female pediatrician for two examinations and a male internist for an exam in between.)

Figure 10. Normalization into two tables, one for exam-specific information and one for examiner-specific information. (Note that Dr. Novello cannot change specialty or gender between examinations.) “DocID” functions as a second foreign key in the “Exam” table. (The other foreign key is “SubjectID”.)

Figure 11. Relationships diagram showing the relationships between the table of subjects (Baby), the table of measurements (Exam) and the table of examiners (Doctor). The “Exam” table functions as a linkage or join table between “Baby” and “Doctor” creating a “many-to-many” relationship between study subjects and examiners.

One-to-One Relationship: Infants and Deaths.

Figure 12. Some fields are subject specific but valued for only a few subjects. Maintaining columns for these fields in the table of subjects leads to empty fields and wasted space.

Figure 13. Creating a separate table with a one-to-one relationship eliminates the empty fields and wasted space.

Figure 14. The relationships diagram now includes a table (“Death”) with a one- to-one relationship with the table of subjects (“Baby”). A subject can only have one record in the one-to-one-related table, but the vast majority of subjects will not have any “Death” record.

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.

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

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.

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

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.

“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 *Not covered in lab this year

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.

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.

Demonstration Option group for examiner’s medical specialty

Guidelines for Data Management in Clinical Research Establish the database tables, their rows and columns, and their relationships correctly at the outset. A poorly organized database makes data maintenance and retrieval nearly impossible. Make sure the data are normalized. The data structures should never require duplicate data entry or redundant storage. ? MS Genetics Example

Guidelines for Data Management in Clinical Research Establish and follow naming conventions for columns and tables. Short field names without spaces or underscores are convenient for programming, querying, and other manipulations. Instead of spaces or underscores, use “IntraCaps” (upper case letters within the variable name) to distinguish words, e.g. “StudyID”, “FName”, “FdDisord”, or “ExamDate”. Table names should be singular, e.g. “Baby” instead of “Babies”, “Exam” instead of “Exams”.

Guidelines for Data Management in Clinical Research Obtain baseline demographic and clinical information about members of the study population from existing computer databases. Avoid re-entering data which are already available (in digital formats) from other sources. In the JIFee Study, the patient demographic data and contact information are obtained from the hospital database. Computer systems can almost always produce text-delimited or fixed-column-width character files that the database management system can import.

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. 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. Examples: ATM Machine’s printed transaction record, Gas Station’s printed receipt

Guidelines for Data Management in Clinical Research Back up the database regularly and check the adequacy of the back up procedure by periodically restoring a file from the back up medium.

Desktop DBMS Microsoft Access Claris Filemaker Pro Paradox Microsoft Visual FoxPro Dataease The processing of records is done by the desktop. The server simply stores files (file server).

Client-Server DBMS Microsoft SQL Server Oracle Informix Sybase The processing of records is done by the server. The desktop manages the screen, but passes queries on to the server. (Just to confuse things, MS Access can be a client for SQL Server, and other enterprise systems. The ultimate in “thin” clients is a browser (Internet Explorer). In this case, the server is an intranet or internet database server.)