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

Journal name:
Nature Biotechnology
Volume:
29,
Pages:
411–414
Year published:
DOI:
doi:10.1038/nbt.1837
Received
Accepted
Published online

Abstract

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

Figures

  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.

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Author information

Affiliations

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

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

Contributions

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.

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