Paul Harris, PhD. Planning, Collecting and Managing Data For Clinical And Translational Research

Transcription

1 Planning, Collecting and Managing Data For Clinical And Translational Research Paul Harris, PhD Associate Professor Department of Biomedical Informatics Vanderbilt University

2 Agenda Data Planning for Clinical and Translational Research REDCap - Practical Tools for Research Data Planning, Collection and Management REDCap Consortium - A Model of Collaboration Supporting Research Around the World REDCap Demonstration + Discussion

3 Important Concept Data planning is a wise investment for your research study Garbage In Garbage Out

4 Important Concept Always, clearly define all data you will collect before seeing the first research subject. It s an ethics thing!

5 Good Practice Develop your primary hypothesis, then fully define primary and secondary outcome measurements necessary to test hypothesis.

6 Good Practice Be exact and discrete when defining variables. This is a really simple study ---- HINT: think about a representative number + units Blood Pressure Systolic BP (mmhg) + Diastolic BP (mmhg) Metabolic Panel Individual Measurements (with units) Medications Beta-Blocker (Yes/No) and/or Dosage

7 Good Practice Review each measurement for best data type. Nominal (Sex, Race) Ordinal (Stage of Cancer, Education Level) Continuous (Temperature, Systolic Blood Pressure) *Waveform - ECG *Images MRI *High density data usually stored intact. Store in your database those outcome measurements derived from the raw data (e.g. avg QT interval, tumor dimension) that will be used in statistical analyses.

8 Good Practice Code categorical variables during data collection. It helps in later analysis. Gender: 0=Male, 1=Female Education: 0 = no high school; 1 = high school; 2 = some college; 3 = college degree Be consistent! Similar questions should use similar codes. Keep a current copy of your codebook for use during statistical analyses.

9 Good Practice Always include a set of confounding factors (e.g. race, gender, date of birth age, height, initial weight, smoker, etc) when planning data collection. These data may be used later to adjust for factors in final data analysis or to check validity of randomization. Consider DNA collection if appropriate for the study. Collection is cheap (you don t have to sequence) and may later help identify study outliers even if you do not initially suspect a genetic component. Remember IRB approval.

10 Good Practice Store raw data fields rather than calculated values. BMI Height + Weight Hypertension: Y/N SBP & DBP You can always calculate BMI from Height and Weight later, but you cannot calculate Height/Weight from BMI DO NOT code continuous variables ---- store them intact.

11 Good Practice Allow for the possibility of missing data in data fields. Never code a zero or other possible value as default. Develop codes for missing values if context is needed Patient doesn t remember (888) Patient will not disclose (999) Dates can be particularly problematic When were you diagnosed? April ; Spring 2003; 2003; In last 5-years

12 For quantitative studies, minimize open-ended free-form text data entry fields. Unstructured data is difficult to analyze and not terribly useful at the end of a study. Good Practice Worst case never analyzed. Best cases domain expert has to recode.

13 Good Practice Develop standard operating procedure manuals for your study. All data collection forms should be reviewed by all collaborators and study managers prior to study start. Case report form design should be iterative and targeted to make things easy and clear for research personnel collecting the data. Build in time for in-service training. Perform at least one full test. Consider full cycle testing on a small representative group of subjects.

14 Good Practice Confidential Data: You may store identifiers in a database / analysis file so long as the application is in a secure location with appropriate security safeguards. Do not sensitive data and always review a dataset for identifiers before sending. HIPAA identifiers (18) are good starting point when identifying confidential fields in your dataset.

15 Data Sharing / Exchange Good Practice Sharing data requires a well-defined data dictionary (including standards encoding where possible) and standard operating procedures. Data coding standards are not welldefined in every scientific research domain, but you can always maintain a structured data dictionary for a study.

16 Good Practice Data Integrity and Validation Use software that checks data type and ranges when accepting data from end-user. Double-data entry (two pass verification) can be useful when accuracy is especially important. =?

17 Good Practice Keep audit trails of all data changes. Paper and computer logs of who added or changed data. Same rules for electronic records

18 Note that software can be ready for 21CFR Part- 11, FISMA, etc --- but certification is a process with other requirements (including much documentation) This is difficult... (audit trails, data validation, double-data entry ) but very important for scientific integrity and it gets even harder for FDA, HHS or VA trials 21CFR Part 11 FISMA

19 Agenda Data Planning for Clinical and Translational Research REDCap - Practical Tools for Research Data Planning, Collection and Management REDCap Consortium - A Model of Collaboration Supporting Research Around the World REDCap Demonstration + Discussion

20 REDCap (Research Electronic Data Capture) Project History 2004 Needs Assessment Researchers needed help managing data for small/medium sized non-trial research projects (pilot, R01, PPG) Hypothesis Researchers will do the right thing (secure, audit trails, etc) if provided an easy way to get needed tools Problem Many projects, few resources

21 REDCap Project History Solution: Metadata-driven application (no per-project programming) First REDCap project operational at Vanderbilt REDCap Consortium Launched REDCap Consortium to share with other universities and foster collaboration for future development

22 Data Entry Survey + CRFs PDFs Visual Status Data Validation Numerous Field Types Human Readable Labels + Text (Free) (Number) (Phone) (Zip) (Date) +TextArea +Select +Radio +File Auto-Variable Coding Branching Logic

23 Data Export + De-ID Tools Embedded De-Identification Tools Exports Raw Data + Stats Script Files (Labels, Coding

24 Many Functional Modules

25 Why REDCap? But Powerful! e.g. Data De-Identification Services Participant Scheduling Support Data Transfer Services & API Graphical Data Review Double-Data Entry Full audit trails and logging Multi-site Data Collection Shared Library -- Supports Standardized Collection Facilitated De-Centralization Constantly Evolving

26 Why REDCap?

27 Why REDCap? Improves science Workflow for creating projects includes all research team members and helps solidify the data management plan before enrolling the first patient. Improving Data Improves Science Improves Health

28 Why REDCap? Proven track record supporting diverse studies Community Research Basic Research Clinical Research Research Operations Global Health

29 Why REDCap? Promotes Standards and Standardization Interface with External Clinical Systems Data Transfer Service: Variable Mapping Data Transfer Service: Adjudication to support de-centralized data collection (multi-center studies)

30 Why REDCap? Junior Faculty / Trainees - Great for teaching research data management

31 Why REDCap? Flexible: REDCap doesn t know or care if a record represents a patient, mouse, cage (solid tools + smart people amazing things happen) Great for Multi-Center Studies Centralized: Use Data Access Groups to Sequester De-Centralized: Metadata = Metadata (ad hoc standards) Writing a grant or going to IRB? Boilerplate language & create a REDCap shell project for screenshot

32 Agenda Data Planning for Clinical and Translational Research REDCap - Practical Tools for Research Data Planning, Collection and Management REDCap Consortium - A Model of Collaboration Supporting Research Around the World REDCap Demonstration + Discussion

33 Consortium Model ( Nov 2009 Sep 2012 (Today) 4,077 60,080 End-Users Institutional Partners

34 Why REDCap? Flexible: REDCap doesn t know or care if a record represents a patient, mouse, cage (solid tools + smart people amazing things happen) Great for Multi-Center Studies Centralized: Use Data Access Groups to Sequester De-Centralized: Metadata = Metadata (ad hoc standards) Writing a grant or going to IRB? Boilerplate language & create a REDCap shell project for screenshot

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36 Agenda Data Planning for Clinical and Translational Research REDCap - Practical Tools for Research Data Planning, Collection and Management REDCap Consortium - A Model of Collaboration Supporting Research Around the World REDCap Demonstration + Discussion

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