1. Proprietary & Confidential 1 Drug Efficacy in the Wild Tim Vaughan 17 June 2011

2. Proprietary & Confidential 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. Proprietary & Confidential 3 PatientsLikeMe web site

4. Proprietary & Confidential 4 PatientsLikeMe background – Three brothers

5. Proprietary & Confidential 5 Stephen Heywood (alsking101)

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

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

8. Proprietary & Confidential 8 The observational study germinates

9. Proprietary & Confidential 9 Timeline

10. Proprietary & Confidential 10 Patients track their progress

11. Proprietary & Confidential 11 The “kitchen sink” plot

12. Proprietary & Confidential 12 Random control may not be a “patient like me”

13. Proprietary & Confidential 13 Demographics – age

14. Proprietary & Confidential 14 Demographics – onset site

15. Proprietary & Confidential 15 Demographics – sex

16. Proprietary & Confidential 16 Matching algorithm

17. Proprietary & Confidential 17 Matching across the entire sample

18. Proprietary & Confidential 18 Pre-treatment progression bias reduced

19. Proprietary & Confidential 19 Results of lithium treatment

20. Proprietary & Confidential 20 Kaplan-Meier for patients & data

21. Proprietary & Confidential 21 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

22. Proprietary & Confidential 22 What Mike (and PatientsLikeMe) can learn

23. Proprietary & Confidential 23 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