Best Practices for Curating Measures in caDSR

1. Best Practices for Curating Measures in caDSR Using UML Models and CRFs Mary Cooper, SAIC July, 2010

2. Overview • Measures in Population Science and Clinical Trials • Curating Questionnaires • Approaches • Perceived Stress Scale • Naming Conventions • Classifications • Classes • Attributes • Value Domains • Definitions • Other Best Practices • Summary

3. Measures in PopSci and CTMS • Measures in Clinical Trials • Included as part of CRFs • Quality of Life • MMQL • Smoking/Alcohol • Demographics • Behavioral and Social Science Measures • Questionnaires and Surveys • National Health Interview Survey • Health Information National Trends Survey • Subjective Numeracy Scale • Perceived Stress Scale • FACT-G

4. Curating Questionnaires Approach Curation of CRFs and Questionnaires follows the business process • Analyze source document • Choose appropriate curation method • Create working spreadsheet or develop UML model • Search caDSR for existing metadata • Identify new items for curation • Consult with SME to vet metadata • Register metadata in caDSR Unique considerations for Questionnaires

5. Perceived Stress Scale • Curation challenges • Capturing concepts • Respecting validity/reliability • Definitions • Likert Scales in value domains • Value meaning concepts • Data Types

6. Perceived Stress Scale

7. Package Naming Convention (caDSR Classification Schemes) • Packages = Class Scheme Items in caDSR • Follow Java package naming conventions; • Hierarchical and lowercase • Naming pattern with levels in the hierarchy separated by periods (.) • Best Practice: • gov.nih.nci.dccps.measurename_measureaccronym

8. Classes • Attributes • Each class must have an attribute for “id” with data type “string” • Class Naming Conventions • The class name should be the measure name (ex., SubjectiveNumeracyScale) • If there are sub scores or total scores a separate class should be added to capture these using measure name appended with “_Score” (ex., subjectiveNumeracyScale_Score) • Class Definitions • Use the descriptions of the scales from GEM as the class definitions

9. Attributes • Attribute Naming Conventions • Capture as much detail as possible • Respect the order of words in the questions • Interpretation of the words considered slang is acceptable • “In the past month, how often have you felt that you were on top of things?” • controlResponsibilitiesPastMonthFrequencyScore • Use “Score” for Likert scales • Add qualifier to describe what the score measures • Include time periods

10. Attributes Attribute Definitions • Process BPs • Include information from the question and instructions • Use SIW to insert the definitions • Question definition templates • A person’s stated perception of (question intent), using a X-point Likert scale. • Total score definition templates • The (mean or sum) of all responses by an individual to the “measure name” Scale. • Sub score definition templates • The (mean or sum) of all responses by an individual to the “domain name” domain of the “measure name” Scale

11. Value Domains • Scale • Definition in caDSR • “An ordered reference standard used to measure incremental changes.” • Likert scale • Alternate definition in PopSci • A set of questions that have been validated as reflecting some abstraction • Score • A numeric value associated with a respondent’s answer to a question or set of questions. • Best Practices • Attribute names end in score • Local value domain classes end in scale • Attributes capturing data about the measure have both

12. Value Domains • Naming Conventions • Use concepts in attributes to describe local value domains • Represent the Likert scale as “_XPointScale” using “Scale” as the primary representation term and “X” as the number of items in the scale • HowGood_6PointScale • Describing Likert scale value meanings • Not at all good = 1 2 3 4 5 = Extremely good • Values are not explicitly described between the endpoints • Define the value meaning in relationship to the scale • 2 = A 2 on a five-point scale from not at all good to extremely good • Data Types • Values represented as numbers in Likert scales • Use “int” (java.int) as the data type in EA

13. Other Best Practices • Associations • Associations should be created between the “question” class and the “score” class using a one to one relationship. Name each end using the class name • Reference Documents • Scoring algorithm should be included in the reference document or derived variable definition.

14. Summary • Touch points • Measures used in PopSci and Clinical Trials • UML model and manual curation methods • Shared domain expertise • CTMS process for caDSR curation • PopSci vocabulary/concepts/definitions • BPs for naming/defining/describing caDSR metadata • Next Steps • Document BPs for use by caDSR users

15. Acknowledgements • Working Group Leads • Jessica Bondy • Mike Collins • Hua Min • Members • Sana Ahmed • Nancy Avis • Mary Cooper • Paul Courtney • Richa Gandhi • Cindy Helba • Bob Lanese • Rick Moser • Riki Ohira • Dianne Reeves

16. Resources/References • caBIG Training Portal • https://cabig.nci.nih.gov/training/?pid=primary.2006-07-07.4911641845&sid=secondary.2006-10-24.0611379107&status=True • https://wiki.nci.nih.gov/display/COREtraining/caCORE+Training+Wiki • caCORE SDK Programmer’s Guide v4.2.1, Chapter 10 • https://gforge.nci.nih.gov/docman/index.php?group_id=148&selected_doc_group_id=5499&language_id=1 • https://gforge.nci.nih.gov/docman/view.php/148/20910/caCore_SDK_4.2.1_ProgrammersGuide_rv1.pdf • VCDE Presentation, “What Do Forms Curators Do?” • https://gforge.nci.nih.gov/docman/index.php?group_id=357&selected_doc_group_id=5793&language_id=1 • GForge UML Model Projects • http://gforge.nci.nih.gov/frs/?group_id=64 • DRAFT Best Practices for Curating Measures • https://gforge.nci.nih.gov/docman/index.php?group_id=623&selected_doc_group_id=5855&language_id=1

17. Comments/Questions • Thank you!