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Real-World Evidence: What Is It and Why Is It so Important In MS?

Real-World Evidence: What Is It and Why Is It so Important In MS?. This program will include a discussion of data that were presented in abstract form. These data should be considered preliminary until published in a peer-reviewed journal. Introduction.

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Real-World Evidence: What Is It and Why Is It so Important In MS?

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  1. Real-World Evidence: What Is It and Why Is It so Important In MS?

  2. This program will include a discussion of data that were presented in abstract form. These data should be considered preliminary until published in a peer-reviewed journal.

  3. Introduction

  4. Information Gained From RCTs and Observational Data

  5. Information Gained From RCTs and Observational Data: Limitations

  6. Sources of RWE: Advantages and Limitations

  7. Checklist for High Quality RWE Studies

  8. Summary of Key Points

  9. Sources of RWE: Structured Databases

  10. MSBase Registry

  11. MSBase: Learnings About Effect of Early Treatment on Conversion to SPMS

  12. Potential for Learning About Treatment Optimization

  13. MS Registries and Cohorts: A Sample

  14. Treatment-Related Data Collection in Germany: PANGAEA Observational Study

  15. Need for Widespread Data Collection: Complementary to RCTs

  16. Advantages and Challenges of RCTs and RWE

  17. Propensity Score

  18. Propensity Score Matching

  19. Propensity Score Matching: Controlling for Variables

  20. Propensity Score-Based Methods in Cardiology

  21. Propensity Score-Based Method: Coffee and Mortality

  22. Distribution of Propensity Scores Before and After Matching

  23. MSBase Propensity-Matched Data: Escalation to Natalizumab vs Fingolimod in Active RRMS

  24. Propensity-Matched Comparison of Sick Days: PEARL and PANGAEA Observational Studies

  25. Bias in Observational Studies: Checklist

  26. Smoking and MS Progression: Swedish National MS Registry

  27. Swedish Registry Disability Outcomes: Developing an Age-Related MS Severity Score

  28. Vast Amounts of Data Are Needed

  29. Individualized Treatment and Big-Data RWE: A Combined Approach

  30. Impactful RWE Requires Structured Data Collection

  31. Concluding Remarks

  32. Abbreviations

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