Data Management Center (DMC) Stan Azen PhD – Director Carolee Winstein PhD, PT, FAPTA – Principal Investigator James Ba

1. Data Management Center (DMC) Stan Azen PhD – Director Carolee Winstein PhD, PT, FAPTA – Principal Investigator James Baurley – DMC Representative

2. Overview: • PART I - Data Management Center • PART II - PTClinResNet Website • PART III - Development Process • PART IV - Structure of DMSC Report • PART V - Project Management • PART VI - Future Plans

3. PART I - Data Management Center • Organization of the DMC • Responsibilities of the DMC

4. Organization of the DMC Coordinators • Stan Azen PhD • Carolee Winstein PhD • Samantha Underwood & Patricia Pate Informatics Team • James Baurley • George Martinez • Mike Hutchinson Statistical Analysis Team • Carolyn Ervin PhD • Tingting Ge

5. Organization of the DMC Data Entry Team • Chris Hahn • JoAnne de los Reyes • Frances Chien • Karina Kunder • Jason Villareal

6. Responsibilities of the DMC • Finalized the four study protocols with regard to design issues, sample size requirements, statistical analysis methods. • Developed the PTClinResNet website • Defined and built the public and secure sections • Organized the network into a user friendly interface

7. Responsibilities of the DMC • Designed and implemented study databases and web-based data entry application • Developed the randomization procedures • Developed a prototype template for reporting progress and safety information to the Data Monitoring Safety Committee (DMSC) • Statistical analyses, quality control and reporting

8. PART II - PTClinResNet Website • Located at: • http://pt.usc.edu/clinresnet • Features of • Public Website • Secure Website • Example - Reports Availability • Document Management • Example - Request for Documents

10. Features of Public Website • Overview of network and study information • Background and responsibilities of key personnel • News items, conference information and announcements • Information for potential study participants

11. Features of Secure Website • Manual of Procedures for each study • Reports to the Steering Committee and PT Foundation • Minutes of conference calls with Study Investigators • Recruitment Status • Reports to the Data Monitoring and Safety Committee

13. Document Management System • Manages PTClinResNet documents. • Designed to limit read and write access to documents based on user groups. • Interested researchers can request access. • Currently in development by Statistical Consultation and Research Center.

15. PART III – CLINICAL TRIAL DEVELOPMENT PROCESS • OVERVIEW • MANUAL OF PROCEDURES (MOP) • DATA ACQUISITION DESIGN • IMPLEMENTATION • DATA ENTRY AND QUALITY CONTROL • DATA ANALYSIS • DATABASE STATISTICS

16. Network Diagram

18. MANUAL OF PROCEDURES (MOP) • Provides a central document for the procedures of a clinical study • Specific Aims • Relevant scientific rationale • Study design and statistical methods • Procedures (randomization, data management, standardization, test administration, protection of subjects, etc.)

19. DATA ACQUISITION DESIGN INSTRUMENTS Designed for accurate and complete data collection DATA DICTIONARY A data structure that stores metadata, i.e., a code book containing information about the data being collected. The data dictionary includes the variable name, data type, allowable and missing codes, value ranges, algorithms, and dataset and version information.

20. DATA ACQUISITION DESIGN • Created data collection forms in collaboration with investigators • Developed multi-study forms and study-specific forms • Multi-study forms utilize common data definitions (variable names and codes) • Developed system for creating unique study and site-specific patient identification numbers • Developed “allowable” and “missing” coding system for all variables

21. INSTRUMENT

22. DATA ACQUISITION DESIGN • Create Data Dictionaries in collaboration with investigators. Data Dictionary fields include: • variable name • data type (numeric, date, character), • allowable and missing codes • range • field length • whether the variable is required • question as it appears on the form • versioning • dataset name

23. DATA DICTIONARY

24. DATA DICTIONARY IN SQL

25. IMPLEMENTATION • Requirements for building physical database: • Final version of data collection forms • Final version of data dictionary • Final version of “business rules” • Properties of database: • Security and menu-navigation, automated range checking, and auditing of users and changes in data values • Training • Manual for data entry • Available on website

26. DATA ENTRY APPLICATION

27. System Accessibility • Designated users for data entry and statistical analysis • Customized security of research datasets, studies, and sites • Research data restricted for blinded evaluators prior to trial completion.

28. Data Entry Process • Develop log sheet to track data.  • Includes dates of collection, data entry, data checking, data entry corrections. • Maintains identifier of tracking personnel • Data : • Form received and logged • Form filed in locked filing cabinets • Entered into SCRC data entry system • Checked by comparing original data form to the data completeness report • Corrected data entered

29. Quality Control Procedures • Certification of evaluators • Range and coding checks built into the data entry system • Cross-sectional and longitudinal quality control checks at the SAS level • Data completeness report

30. Data completeness/ quality reports Example – MUSSEL Education Form Data

31. DATA ANALYSIS • ODBC-Compliant statistical packages (SAS, SPSS, STATA) allow real time access to the study data and data dictionary. • Permits powerful control over data using Structured Query Language (SQL)

32. DATA FLOW DIAGRAM

33. Database Statistics - June 2005 • 2202 Variables • 92 Shared • 398 STEPS specific • 478 MUSSEL specific • 557 STOMPS specific • 677 PEDALS specific • 62 Datasets

34. PART IV - Structure of DMSC Report DMSC Reporting • Summary of Study Design • Objective • Subjects • Sample Size • Treatments • Follow-up • Endpoints – Primary & Secondary • Summary of Analytic Plan

35. Structure of DMSC Report DMSC Reporting • Summary of Progress and Results Example - PEDALS • Screening Trial Profile • Total Subject Enrollment by Month of Study • Primary Reasons for Ineligibility or Refusal • Summary of Enrollment by Strata • Baseline Demographics • Compliance - Intervention • Summary of Clinical Events

36. Study Profile PEDALS Screening Trial Profile

37. Primary Reasons for Ineligibility or Refusal Example - PEDALS

38. Recruitment Status PEDALS - Total Subject Enrollment by Month of Study

39. Baseline Characteristics PEDALS Baseline Demographics *Mean for continuous variables; frequency (%) for categorical variables

40. Compliance PEDALS Compliance - INTERVENTION

41. Adverse Events Summary of Clinical Events

42. PART V - Project Management • DATA REQUESTS • PROJECT SCHEDULE • Example - STOMPS

43. Example of Data Request Form

44. Protocol For Data Requests • Process for data request – • Data request send to DMC • Ticket number assigned & recorded • Request - prioritized & assigned • Request resolved • Notification emailed to requestor

45. Project Schedule STOMPS Example

49. PART VI - Future Plans • Complete Primary Analysis • Coordinate and Schedule Secondary Analyses • Papers – Study Specific and DMC • Implement New Studies: • LEAPS • ICARE

50. DMC Papers - In Development DESCRIPTION OF A CLINICAL RESEARCH NETWORK FOR THE EVALUATION OF PHYSICAL THERAPY INTERVENTIONS James Baurley, Carolyn Ervin, Tingting Ge, Stanley Azen, Carolee Winstein Departments of Preventive Medicine and Biokinesiology and Physical Therapy University of Southern California, Los Angeles CA USA BAYESIAN META-ANALYSIS OF EFFECTS OF STRENGTH TRAINING INTERVENTIONS ON FUNCTION IN PATIENTS WITH PHYSICAL DISABILITIES James Baurley, Stanley Azen, David Conti, Carolee Winstein, Carolyn Ervin Departments of Preventive Medicine and Biokinesiology and Physical Therapy University of Southern California, Los Angeles CA USA ASSESSMENT OF THE COMPARABILITY OF THE TWO VERSIONS OF SF-36 IN A PHYSICAL THERAPY CONTEXT Tingting Ge, Stanley Azen, Carolyn Ervin, James Baurley,Carolee Winstein Departments of Preventive Medicine and Biokinesiology and Physical Therapy University of Southern California, Los Angeles CA USA