Accelerated clinical discovery using self-reported patient data collected online and a patient-matching algorithm

Journal name:
Nature Biotechnology
Year published:
Published online


Patients with serious diseases may experiment with drugs that have not received regulatory approval. Online patient communities structured around quantitative outcome data have the potential to provide an observational environment to monitor such drug usage and its consequences. Here we describe an analysis of data reported on the website PatientsLikeMe by patients with amyotrophic lateral sclerosis (ALS) who experimented with lithium carbonate treatment. To reduce potential bias owing to lack of randomization, we developed an algorithm to match 149 treated patients to multiple controls (447 total) based on the progression of their disease course. At 12 months after treatment, we found no effect of lithium on disease progression. Although observational studies using unblinded data are not a substitute for double-blind randomized control trials, this study reached the same conclusion as subsequent randomized trials, suggesting that data reported by patients over the internet may be useful for accelerating clinical discovery and evaluating the effectiveness of drugs already in use.

At a glance


  1. Conceptual overview of the online study environment and matching algorithm.
    Figure 1: Conceptual overview of the online study environment and matching algorithm.

    (a) The number of patients choosing to experiment with lithium carbonate peaked in the months after publication of a small clinical trial in Italy. Preliminary negative results from this patient-led study were announced before the first randomized control trial had started recruitment. (b) Aggregate view of FRS scores for all 348 patients analyzed in this study. These data were publicly available online during the study. (c) Illustration of disease progression curves of control individuals that are good and poor matches for a particular patient. Both control individuals would be considered comparable by traditional matching criteria. The PatientsLikeMe matching algorithm minimizes the area between the disease progression curves for a target patient and a control.

  2. Results of analyses show no significant effect of lithium carbonate on rate of ALS progression.
    Figure 2: Results of analyses show no significant effect of lithium carbonate on rate of ALS progression.

    (a) Summary of pretreatment disease progression curves for 149 intent-to-treat patients matched by the PatientsLikeMe matching algorithm. Error bars are 1 s.e.m. in each direction. (b) Intent-to-treat analysis of 149 patients treated with lithium carbonate compared with controls fails to find any significant differences in progression (P > 0.05 at 12 months). Squares represent data from a previous trial7. Error bars are 1 s.e.m. in each direction. Dashed lines indicate the smallest detectable effect (α = 0.05, 80% power). (c) Full-course analysis of 78 patients treated with lithium carbonate compared with controls fails to find any significant differences in progression (P > 0.05 at 12 months). Dashed lines as in b.


  1. Ross, E. Unapproved drug use: compassionate or cause for concern? Lancet Neurol. 8, 136137 (2009).
  2. Boguski, M.S., Mandl, K.D. & Sukhatme, V.P. Drug discovery. Repurposing with a difference. Science 324, 13941395 (2009).
  3. Miller, R.G. et al. Outcomes research in amyotrophic lateral sclerosis: lessons learned from the amyotrophic lateral sclerosis clinical assessment, research, and education database. Ann. Neurol. 65 (Suppl 1), S24S28 (2009).
  4. Regenberg, A.C., Hutchinson, L.A., Schanker, B. & Mathews, D.J. Medicine on the fringe: stem cell-based interventions in advance of evidence. Stem Cells 27, 23122319 (2009).
  5. ALSUntangled Group. ALSUntangled No. 8: Low dose naltrexone for ALS. Amyotroph. Lateral Scler. 12, 7678 (2011).
  6. Wijesekera, L.C. & Leigh, P.N. Amyotrophic lateral sclerosis. Orphanet J. Rare Dis. 4, 3 (2009).
  7. Fornai, F. et al. Lithium delays progression of amyotrophic lateral sclerosis. Proc. Natl. Acad. Sci. USA 105, 20522057 (2008).
  8. Figlewicz, D.A. Comments on Fornai et al. (PNAS/ Feb 2008). Amyotroph. Lateral Scler. 9, 125126 (2008).
  9. Meininger, V., Shefner, J. & Cudkowicz, M. Lithium therapy in ALS. Amyotroph. Lateral Scler. 9, 122 (2008).
  10. Bedlack, R.S., Maragakis, N. & Heiman-Patterson, T. Lithium may slow progression of amyotrophic lateral sclerosis, but further study is needed. Proc. Natl. Acad. Sci. USA 105, E17 (2008).
  11. Frost, J.H., Massagli, M.P., Wicks, P. & Heywood, J. How the Social Web Supports patient experimentation with a new therapy: the demand for patient-controlled and patient-centered informatics. AMIA Annu. Symp. Proc. 6, 217221 (2008).
  12. Cedarbaum, J.M. et al. The ALSFRS-R: a revised ALS functional rating scale that incorporates assessments of respiratory function. BDNF ALS Study Group (Phase III). J. Neurol. Sci. 169, 1321 (1999).
  13. Wicks, P. et al. Sharing health data for better outcomes on PatientsLikeMe. J. Med. Internet Res. 12, e19 (2010).
  14. Cudkowicz, M.E. et al. Toward more efficient clinical trials for amyotrophic lateral sclerosis. Amyotroph. Lateral Scler. 11, 259265 (2010).
  15. Castrillo-Viguera, C., Grasso, D.L., Simpson, E., Shefner, J. & Cudkowicz, M.E. Clinical significance in the change of decline in ALSFRS-R. Amyotroph. Lateral Scler. 11, 178180 (2010).
  16. Aggarwal, S.P. et al. Safety and efficacy of lithium in combination with riluzole for treatment of amyotrophic lateral sclerosis: a randomised, double-blind, placebo-controlled trial. Lancet Neurol. 9, 481488 (2010).
  17. Chio, A. et al. Lithium carbonate in amyotrophic lateral sclerosis: lack of efficacy in a dose-finding trial. Neurology 75, 619625 (2010).
  18. Verstraete, E., Veldink, J.H. & van den Berg, L.H. Would riluzole be efficacious in the new ALS trial design? Lancet Neurol. 9, 949950, author reply 950–951 (2010).
  19. Armon, C. Is the lithium-for-ALS genie back in the bottle? Neurology 75, 586587 (2010).
  20. Kriz, J., Nguyen, M.D. & Julien, J.P. Minocycline slows disease progression in a mouse model of amyotrophic lateral sclerosis. Neurobiol. Dis. 10, 268278 (2002).
  21. Van Den Bosch, L., Tilkin, P., Lemmens, G. & Robberecht, W. Minocycline delays disease onset and mortality in a transgenic model of ALS. Neuroreport 13, 1067 (2002).
  22. Gordon, P.H. et al. Placebo-controlled phase I/II studies of minocycline in amyotrophic lateral sclerosis. Neurology 62, 1845 (2004).
  23. Gordon, P.H. et al. Efficacy of minocycline in patients with amyotrophic lateral sclerosis: a phase III randomised trial. Lancet Neurol. 6, 10451053 (2007).
  24. Montes, J. et al. Development and evaluation of a self-administered version of the ALSFRS-R. Neurology 67, 12941296 (2006).
  25. Logroscino, G. et al. Descriptive epidemiology of amyotrophic lateral sclerosis: new evidence and unsolved issues. J. Neurol. Neurosurg. Psychiatry 79, 611 (2008).
  26. Gordon, P.H. et al. Defining survival as an outcome measure in amyotrophic lateral sclerosis. Arch. Neurol. 66, 758761 (2009).
  27. Call, J., Scherzer, N.J., Josephy, P.D. & Walentas, C. Evaluation of self-reported progression and correlation of imatinib dose to survival in patients with metastatic gastrointestinal stromal tumors: an open cohort study. J. Gastrointest. Cancer. 41, 6070 (2010).
  28. Lindstrom, M.J. & Bates, D.M. Newton-Raphson and EM algorithms for linear mixed-effects models for repeated-measures data. J. Am. Stat. Assoc. 83, 10141022 (1988).

Download references

Author information


  1. Research and Development, PatientsLikeMe Inc., Cambridge, Massachusetts, USA.

    • Paul Wicks,
    • Timothy E Vaughan,
    • Michael P Massagli &
    • James Heywood


P.W. designed the study, oversaw the project and drafted and revised the manuscript. T.E.V. developed the matching algorithm, analyzed data and revised the manuscript. M.P.M. designed the lithium data capture tool, developed statistical methods and revised the manuscript. J.H. designed the study, designed the ALS web site, developed the matching algorithm and revised the manuscript.

Competing financial interests

P.W., T.V., M.M. & J.H. are employees of PatientsLikeMe and own shares and/or stock options in the company.

Corresponding author

Correspondence to:

Author details

Supplementary information

PDF files

  1. Supplementary Text and Figures (1 MB)

    Supplementary Figs. 1 and 2

Zip files

  1. Supplementary Data (2 MB)

    Supplementary Data

Additional data