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Challenges Facing the Programmer in Observational Research

Challenges Facing the Programmer in Observational Research. Laurence Carpenter, Amgen PhUSE, October 12 th 2011. Agenda. Background Introduction Types of Observational Research (OR) Study Data Format and Collection The Changing Environment Compliance to Standards Conclusion.

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Challenges Facing the Programmer in Observational Research

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  1. Challenges Facing the Programmer in Observational Research Laurence Carpenter, Amgen PhUSE, October 12th 2011

  2. Agenda • Background • Introduction • Types of Observational Research (OR) Study • Data Format and Collection • The Changing Environment • Compliance to Standards • Conclusion

  3. Background • Developing new drugs is getting harder • OR becoming increasingly important • Evidence regarding • Disease • Costs • Utilization patterns • Real world data (safety and effectiveness)

  4. Background (cont ..) • Demand from Regulatory Agencies • Pharmacovigilance • Risk/Benefit • Demand from Reimbursement Authorities • Health Technology Assessments • Comparative effectiveness

  5. Introduction • How are Statistical Programmers affected? • Familiarity with phases I-IV • Different study designs • Challenges

  6. Types of Observational Study • Many Types • Reasons for Choice • Data required • Practical aspects of data collection • Timelines • Schedule of assessments vs Routine clinical care • Prospective vs Retrospective

  7. Prospective Studies • Registries (Longitudinal Cohort Studies) Enrolment End of Study Study Visits & CRF completion

  8. Prospective Studies (cont ..) • Prospective Chart Reviews Enrolment End of Study Standard Clinical Care and Chart Abstraction

  9. Retrospective Studies • Retrospective Chart Reviews • Retrospective Database Analysis Enrolment Standard Clinical Care Chart Abstraction

  10. Data Format and Collection • (e)CRF data • Adjudication data • PRO data • Spreadsheet data • Adding value

  11. Data Quality • Completeness of data • Cleanliness of data • Rate of incoming data • Database analysis

  12. Coding Techniques • Efficient coding • Generally desirable • Not usually critical in clinical trials • May become more important in OR • Techniques (in SAS) include • Avoiding PROC SORT’s where possible • Using ‘WHERE’ instead of ‘IF’ • Creating Indexes • Testing programs using a subset of data

  13. The Changing Environment • Goals and Objectives • Evolution of ideas during study • Pre-specification / ad-hoc work • Programming team may need to work differently • Planning vs Flexibility • Timelines and resourcing • Risks to quality

  14. Use of Output • Clinical Research • Derived Datasets/TFLs usually final deliverables • Clinical Study Report, Submission • Observational Research • Derived Datasets/TFLs may be part of a process • Costings • Economic Modelling • Publications / Posters

  15. Functional Interactions

  16. Functional Interactions (cont ..)

  17. Compliance to Standards • SDTM • ADaM • Project Standards

  18. Conclusions • Increased demand for OR studies • Presents new challenges for programmers • Knowledge • Study designs • Data considerations • Interactions • Flexibility • Project management • Technical • Communication

  19. Questions

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