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Validating a measure of ME/CFS symptomatology - Brown & Jason (DePaul Symptom Questionnaire)

Discussion in 'Latest ME/CFS Research' started by Bob, Jul 24, 2014.

  1. Bob


    England (south coast)
    Validating a measure of myalgic encephalomyelitis/chronic fatigue syndrome symptomatology
    Abigail A. Brown & Leonard A. Jason
    Fatigue: Biomedicine, Health & Behavior: Vol. 2, Iss. 3, 2014
    Published online: 23 Jul 2014

    This paper works towards validating the DePaul Symptom Questionnaire.
    Unfortunately the full paper isn't available for free.

    aimossy, Sasha, biophile and 5 others like this.
  2. Simon


    Monmouth, UK
    Hope to get to see the full text of this
    The crucial thing about all these fancy factor analyses is that they hold up in independent samples, as opposed to being driven by the data in one specific sample. And the 'adequate' description of performance in a second sample isn't exactly reassuring. Though hard to judge from an abstract alone.

    However, the 3-factors identified were previewed in Lenny Jason's presentation to IOM, 5 May 2014 (powerpoint presentation)

    Brown and Jason’s (2014 - ie this published paper, then a submitted manuscript) Study Identified Three Factors:

    jason 3 factor.png

    Interpretation of Brown et al. Study
    •Two of the emergent factors were
    –Neurological/Cognitive Dysfunction
    –Post-Exertional Malaise
    –fit well with previous literature indicating that these are two of the cardinal symptom clusters of ME/CFS

    •One factor was items from Neuroendocrine, Autonomic, & Immune Dysfunction
    more difficult to interpret as it incorporates many symptom clusters

    •This suggests that there may be core, well-defined symptom clusters such as cognitive impairment and post-exertional malaise
    –but also that there may be many other symptoms that are experienced differently by patients

    All of which looks pretty interesting to me: PEM + Neurocognitive as core symptoms common to all patients, plus a group of other symptoms experienced in different selections by different patients.

    added: fun video! Factor analysis in under 2 mins. Basically it's about simplifying the data by finding a small number of underlying common 'factors' that causes individual items to cluster together. Guaranteed maths-free

    online slideshow with quite a lot of detail: Exploratory factor analysis
    nice quote from it
    The whole process of Factor Analysis is very fuzzy, with a lot of scope for, even requirement for, researcher judgement during the analysis (choosing different forks in the path). That's why replication of any factor structure on an independent sample is important.
    Last edited: Jul 25, 2014
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  3. Dolphin

    Dolphin Senior Member

    One general point about factor analysis is the names that are given to factors can be somewhat arbitrary. I've come across cases where a researcher gave particular factors names which made it sound all nice and exact but when one looked at the individual entries, they didn't always match the names for the groups.
    Simon and SOC like this.
  4. Simon


    Monmouth, UK
    Commentary on full text, focusing on symptom clusters
    Validating a measure of ME/CFS symptomology
    [see top of page for abstract]​

    Thanks to Prof Lenny Jason for the full text and for answering questions.

    Although this study makes lots of interesting points about case definitions, its primary purpose was to validate the DePaul Symptom Questionnaire (DSQ). The questionnaire was broadly validated (I'll spare you the tedious statistical details), though the team are planning on tweaking it in the light of these results.

    The intro criticises Fukuda for its polythetic criteria ie no specific mandatory symptoms beyond unexplained fatigue (basically: 'Pick four symptoms. Any four will do'). Then focuses on case definition work by Ramsay, Goudsmit, dowsett and Hyde:
    Jason prefers this approach to the more extensive symptoms required by CCC and ICC. But ultimately believes we need data-driven approaches to find the most appropriate real-world definition.

    The samples: Biobank and DePaul
    The main sample used was of 214 subjects from the CFIDS biobank.These were all "diagnosed by a licensed physician specializing in CFS, ME/CFS and ME care". 74% were female and average age 49. Only 12% were working full or part-time, with 67% on Disability benefit. However, 67% had completed college and 24% also had a graduate or professional degree so this sample is not representative educationally. Note that people volunteer for this, rather than being a random selection and that may skew the results (this educational skew to graduates is true of many studies too eg the recent Andrew Miller fMRI one).

    The DePaul sample (n=189) used to validate the results was a convenience sample, relied on self-report of current ME/CFS rather than physician diagnosis and 40% had a graduate degree (76% any degree), so is not exactly ideal.

    Results: 3 clusters, modest fit with the data
    As covered before, they found three factors/clusters:
    • Neuroendocrine, Autonomic, & Immune Dysfunction
    • Neurological/Cognitive Dysfunction
    • Post-Exertional Malaise

    These tie in broadly with the three symptoms highlighted by the theorists: PEM, neurological and autonomic.

    However, the 'fit' isn't that good. The success of Factor Analysis in identifying strong clusters is measured by "percentage of variance explained" - or how well the clusters account for the actual data. 100% is a perfect result (so never seen) and 0% is the worst. Typically, 65% or more is seen as very good, and 50% is reasonable.

    However, in this study the three clusters only accounted for 42% of the data. The biggest was the broad broad autonomic neuroendocrine/immune cluster accounting for 31% for the - with only 5% accounted for by PEM.

    In stage two of the study, they looked to see how well these three factors derived from the BioBank data fitted the DePaul cohort data. While I have a reasonable understanding of the 'explanatory factor analysis' used in stage one, the 'confirmatory factor analysis' used in stage two is beyond me - so I will take the authors word for it that the fit was "adequate" i.e. OK, but not exactly impressive.

    In summary, it looks like the authors are on to something important about how symptoms cluster,
    but they haven't exactly nailed it yet.

    What this means/Next steps
    A weakness that the authors mention is that the sample size is on the small size for this particular technique. There is debate about how big a sample is needed, but given the number of symptoms included in this analysis, a sample size of roughly 400 or more would probably be ideal (vs 214 used in stage one).

    The good news is that the team are already working on a new analysis using larger datasets.

    Another reason the clusters aren't that strong is that symptoms might not be enough to classify people - maybe you need objective tests too in order to reveal what's really going on.
    Finally, the authors emphasise their view that case definitions should be driven by data:
    To which I would simply add their earlier point about using objective markers alongside symptoms.
    Last edited: Sep 1, 2014
    mango, goldberg, Bob and 4 others like this.
  5. SOC

    SOC Senior Member

    Very informative, Simon! Thanks for doing this very clear summary of this interesting study.

    BTW, does the phrase, at the end of your post, need a NOT in it somewhere?
    Last edited: Aug 30, 2014
    goldberg, Simon, Bob and 1 other person like this.
  6. Bob


    England (south coast)
    @Simon, thanks for all of that. :thumbsup:
    Simon likes this.

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