1 / 22

Unveiling Drug Efficacy in Real-world Settings: Insights from PatientsLikeMe

Explore the impact of lithium on ALS progression through PatientsLikeMe’s observational study. Delve into the biases and results of this innovative approach to patient data analysis, with practical insights on predictive modeling and outcomes.

libby
Download Presentation

Unveiling Drug Efficacy in Real-world Settings: Insights from PatientsLikeMe

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Drug Efficacy in the WildTim Vaughan17 June 2011

  2. Contents • PatientsLikeMe • What can MikeFromFinland teach us, and vice versa? • Lithium delays progression of ALS?! • PatientsLikeMe’s observational study • Finding patients like me • Results • Predictive modeling / What is my outcome? • Concluding remarks

  3. PatientsLikeMe web site

  4. PatientsLikeMe background – Three brothers

  5. Stephen Heywood (alsking101)

  6. What can Mike teach us, and vice versa? Lithium

  7. Lithium delays progression of ALS?! Fornai et al., PNAS 105:2052-2057 (2008)

  8. Timeline

  9. Patients track their progress

  10. The “kitchen sink” plot

  11. Random control may not be a “patient like me”

  12. Demographics – age

  13. Demographics – onset site

  14. Demographics – sex

  15. Matching algorithm

  16. Matching across the entire sample

  17. Pre-treatment progression bias reduced

  18. Results of lithium treatment

  19. Kaplan-Meier for patients & data

  20. Biases and other stuff that worried us • Self-selection for treatment • “Recruitment bias” • Data reported (vs. data opportunity) • Outliers (e.g. PMA and PLS) • “Optimism bias” at treatment start

  21. What Mike (and PatientsLikeMe) can learn

  22. Conclusions • Structured, self-reported patient data, despite being subject to bias (like all patient data!), has value • Think about bias, and then think about bias again (Repeat) • “Pair programming” for statistics

More Related