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Planning and Budgeting for Data Management in a Clinical Research Study

Planning and Budgeting for Data Management in a Clinical Research Study. Michael A. Kohn, MD, MPP 5 February 2008. Outline. Assignment 3 Review Guidelines for Research Databases Loose Ends: BLOBs, Front Ends Planning and Budgeting for Data Management in a Research Project Assignment 4

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Planning and Budgeting for Data Management in a Clinical Research Study

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  1. Planning and Budgeting for Data Management in a Clinical Research Study Michael A. Kohn, MD, MPP 5 February 2008

  2. Outline • Assignment 3 Review • Guidelines for Research Databases • Loose Ends: BLOBs, Front Ends • Planning and Budgeting for Data Management in a Research Project • Assignment 4 • Annette Sohn

  3. Housekeeping • Database demos with advice for Assignment 4: Tuesday 2/12 • Carolyn Jasik • Assignment 4 is due 2/18 • Please try to return the Learn MS Access 2000 CD

  4. Assignment 3 Lab 3: Exporting and Analyzing Data 1/29/2008 Determine if neonatal jaundice was associated with the 5-year neuropsychological scores and create a table, figure, or paragraph appropriate for the “Results” section of a manuscript summarizing the association. Extra Credit: Write a sentence or two for the “Methods” or “Results” section on inter-rater reliability. (Use Bland and Altman, BMJ 1996; 313:744)

  5. Answer Of the infants with neonatal jaundice, 149 had neuropsychological exams at age 5, and of the infants without neonatal jaundice, 248 had neuropsychological exams. The mean (+SD) neuropsychological score was significantly higher in the jaundice group, 111.5 +21.1, than in the no-jaundice group 101.4+20.5 -- difference 10.1 (95% CI 5.9 – 14.4).

  6. Newman T et al. N Engl J Med 2006;354:1889-1900

  7. Would you submit this for publication? ----------------------------------------------------------------------------- Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- No | 248 101.3925 1.303441 20.52661 98.8252 103.9597 Yes | 149 111.5358 1.732576 21.14879 108.112 114.9596 ---------+-------------------------------------------------------------------- combined | 397 105.1994 1.06956 21.31083 103.0967 107.3021 ---------+-------------------------------------------------------------------- diff | -10.14332 2.152007 -14.37414 -5.912502 ------------------------------------------------------------------------------ Degrees of freedom: 395 Ho: mean(No) - mean(Yes) = diff = 0 Ha: diff < 0 Ha: diff ~= 0 Ha: diff > 0 t = -4.7134 t = -4.7134 t = -4.7134 P < t = 0.0000 P > |t| = 0.0000 P > t = 1.0000

  8. Essential Elements • Sample size (149 jaundiced, 248 non-jaundiced) • Indication of effect size (report both means, or the difference between them) • Get direction of effect right (Jaundiced group did better!) • Indication of variability (Sample SDs, SEs of means, CIs of means, or CI of difference between means.)

  9. Browner on Figures Figures should have a minimum of four data points. A figure that shows that the rate of colon cancer is higher in men than in women, or that diabetes is more common in Hispanics than in whites or blacks, [or that jaundiced babies had higher IQs at age 5 years than non-jaundiced babies,] is not worth the ink required to print it. Use text instead. Browner, WS. Publishing and Presenting Clinical Research; 1999; Williams and Wilkins. Pg. 90

  10. Cutoff at 50? Caption should be below figure. What are the error bars? “Neuopsychiatric”

  11. Cutoff at 60? Caption should be below figure.

  12. Browner on 3-D Figures Three dimensional graphs usually are not helpful. Browner, WS. Publishing and Presenting Clinical Research; 1999; Williams and Wilkins. Pg. 97 Also, note that the 3-D is only an effect. The data are two dimensional (score by jaundice).

  13. Takes the prize for ugliest figure.

  14. Caption not sufficiently explanatory. Sample size?

  15. Figure 1: In 149 infants with neonatal jaundice, the average IQ scores were higher compared to the 248 non-jaundiced infants when evaluated at age 5 (p<0.0001).

  16. Box Plot • Median Line • Box extends from 25th to 75th percentile • Whiskers to upper and lower adjacent values • Adjacent value = 75th /25th percentile ±1.5 x IQR (interquartile range) • Values outside the adjacent values are graphed individually • Would be nice if area (or at least width) of box were proportional to sample size (N). In some box plots the width of the box is proportional to log N, but not in Stata.

  17. Extra Credit Extra Credit • Report within-subject SD (4.0) as a measure of reliability. • Calculate repeatability (11.0) • Bland-Altman plot with mean difference and 95% limits of agreement* * Nobody did this.

  18. Methods or Results? We assessed inter-rater reliability of the neuropsychological test scores by having different examiners re-test 198 of the children. The within-subject standard deviation was 4.0, so the “repeatability” was 11.0, meaning that two examiners of the same subject would score within 11 points of each other 95 percent of the time. (Bland and Altman, BMJ 1996; 313:744)

  19. N = 142 (children examined by both Satcher and Richmond) Mean Difference = 0.49 (95% CI -0.41 – 1.38) 95% Limits of Agreement: -10.272 – 11.244

  20. What Have You Learned? • The meaning and importance of the terms “normalization”, “primary key”, and “foreign key”. • The difference between a flat-file database, and a normalized, multi-table relational database. • A little bit of Microsoft Access • Querying data • Exporting data for analysis in a statistical package

  21. Guidelines for Data Management in Clinical Research 1. 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. Try to avoid duplicate data entry or redundant storage. Sometimes it helps to start with the data collection forms, but remember, you do NOT need one table per data collection form. In the labs you learned that one form can combine data from several tables. And data from one table can appear on several forms.

  22. Start with Data Tables or Data Collection Forms? It doesn’t matter as long as the process is iterative. Can start with the tables and then develop the forms, test the forms, find problems, and update the tables. Can start with a word-processed form, create the tables, test, and update.* *This seems to work better for most investigators

  23. Guidelines for Data Management in Clinical Research 2. 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. “SubjectID”, “FName”, or “ExamDate”. Table names should be singular, e.g. “Baby” instead of “Babies”, “Exam” instead of “Exams”.

  24. Guidelines for Data Management in Clinical Research 3. 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.

  25. Guidelines for Data Management in Clinical Research 4. 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.

  26. On-screen vs. paper forms 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

  27. Guidelines for Data Management in Clinical Research 5. Follow 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.

  28. 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.

  29. Guidelines for Data Management in Clinical Research 6.      Back up the database regularly and check the adequacy of the back up procedure by periodically restoring a file from the back up medium.

  30. Demonstration (BLOB) Field types are not limited to numbers, text, dates. You can put an “object”, such as a Word document or a photo, in a field Memo fields in the Infant Jaundice Database Word Document Fields on the “Class” form of the ATCR Student Database Photograph fields in the ATCR Student Database

  31. “Front End” vs. “Back End” “Back End” – Tables and Data “Front End” – Forms and reports for entering and viewing the data Access database that you have been using combines “back end” (tables and relationships) with “front end” (forms and reports). QuesGen uses MySQL for “back end” and PHP for “front end.” Neuro Clinics database uses MS SQL Server for “back end” and Visual Basic for “front end.”

  32. Four Types of Research Database • Combination of paper files, Excel spreadsheets, and direct keyboard entry into the statistical analysis package.* • Desktop multi-table relational database.** • Client-Server or “Enterprise” multi-table relational database.*** • Web-Enabled Research Platform.** *Can do yourself ** Might be able to do yourself ***Definitely need to get help

  33. Desktop multi-table relational database.** • All study data in one database (with many tables) • Proper normalization eliminates redundancy and opportunity for inconsistencies • Can enforce referential integrity • Easy to develop on-screen forms for data entry and viewing • Graphical querying tool • Report writer **Might be able to start yourself. Eventually may need to hire help.

  34. Client-Server or “Enterprise” multi-table relational database.*** • Richer security model • Detailed auditing of data entry and revision • BIG databases • Transaction-intensive databases ***Need to pay somebody to build it.

  35. Web-Enabled Research Platform • Browser based entry from anyplace with an internet connection. • Enterprise database back end • Available as a hosted service • To be demonstrated next week

  36. Approach 1 • Develop prototype forms on paper and using Word • Build a prototype database in Access using what you learned in this course. (You will describe this prototype in Assignment 4.) • Pay someone to help you • Turn your prototype Access database into a production system • Split into front and back ends • Make available via Remote Desktop • Upsize to an enterprise database

  37. Approach 2 • Develop prototype forms on paper and using Word • Build a data collection system using a web-enabled research platform • Extract data from the web platform and use the Access graphical querying tool to filter, organize and format. • Complete analysis with Stata or another statistical package.

  38. Advice on Building a Database for your Study • Budget $500-$1000 per month out of your grant for database consulting during the design phase. • Take advantage of your departmental resources. • Take advantage of campus resources. • Don’t confuse database development with network administration and systems management.

  39. Costs BREAD Data Management Consulting Unit: On-campus resource for database design consulting. $100/hour http://ctsi.ucsf.edu/bread/

  40. Costs The JIFee Study developed a comprehensive database for study administrative data as well as results. They had a full time project coordinator and spent about $10,000 on database consulting. Total cost of the JIFee Database in time and money was at least $25,000.

  41. Departmental Resources • Your department should provide you with a networked desktop computer, as well as network support. (Server access and database hosting is available from the BREAD DMU.) • Your departmental computer person will NOT be able to help you with database design or development. System administrators do not and cannot build database management systems.

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

  43. General Description of Database • DBMS, e.g. MS Access XP • # of dynamic tables • # of static “lookup” tables • # of forms • # of reports An appendix should include the relationships diagram, the table names and descriptions, and the field names and descriptions (data dictionary). Print relationships diagram using either “Print Relationships” or taking a screen shot.

  44. Data Collection and Entry • Import baseline data from existing systems • Import lab results, scan results (e.g. DEXA), holter monitor data, and other digital data. • For each form, who will collect the data? • Collect onto paper forms and then transcribe? Enter directly using screen forms? Scannable forms?

  45. Error Checking and Validation • Database automatically checks data against the range of allowed values. • Periodic outlier detection. (Outliers still within the range of allowed values.) • Calculation checks • Is double data entry really needed ?

  46. Analysis • How will you get the data out of the database?

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