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Comment · 10 years ago
Got around to reading the primary source. I am surprised there isn't more skepticism from this group, but it may have to do with not having time to read the paper. A few reasons to be skeptical of the results (some are stronger than others):

1) First, required reading for anyone interpreting medical studies: http://jama.jamanetwork.com/article.aspx?.articleid=201218 TLDR; is that ~40% of _randomized_ (generally considered highest level of quality) studies and maybe more are proven wrong by subsequent ones resulting from publication bias. Consider that this would not have been published in a high profile journal if it showed no effect, hence pub bias. In other words, we're not seeing the similarly large analyses that show no effect because they're not published.

2) The adjusted analyses have confidence intervals on the odds ratio that overlap with 1.0 except for the highest dose group. There is evidence of a dose response relationship, but there is a problem in how they measure dose response. Cumulative dose biases towards people who have been on medications for a long time. People who are able to take medications (ie are older) for a long time are at greater risk for dementia.

3) It is problematic that they assume any effect on their endpoint is a class effect (ie true of all anticholinergics). The relationship with one medication may be driving this effect entirely. I would like to see, for example, oxybutynin pulled out of the analysis which is never prescribed for allergies and more often for neurological conditions, which could be an unmeasured confounder depending on how they modeled the comorbidities.

4) Controlled analyses are difficult to do well. Most of the time it controls for linear effects, meaning synergistic effects (which are probably involved in development of dementia) are not captured unless explicitly modeled. I do not see that they did this here.

5) I am further suspicious of the way they obtained fill data. As you are noting, many OTC anticholinergics are not "dispensed" in a way typical of prescription anti-cholinergics. If I bought Benadryl at CVS or even if I bought the pre-packaged bottle at the Group Health pharmacy, it's pretty clear to me that neither of these would be reflected in the data. This skews heavily toward prescription anti-cholinergics, which tend to be given for psych/neuro indications which themselves have an association (though likely not a causal one) with dementia. Again, unclear if they modeled all of these.

Overall, I share the sentiment that medical literature needs to be more open. I think it's unfortunate that current state makes it hard to confirm analyses like these with a great degree of certainty.

Comment · 13 years ago
Completely agree with reproducibility of research. Would be great to further improve such algorithms using data collected from the public--selection issues and inaccuracies aside.

The algorithm is in the appendix of the article the tool links to.

Input fields are limited via javascript so may not have worked if you have it disabled. You're right we should do it server side as well.

Thanks for the feedback.

Comment · 13 years ago
Glad you found it useful. Stay tuned for similar tools.
Comment · 14 years ago
Definitely. But with still mostly paper recording, limited data standards, and particularly onerous privacy requirements (justifiably so), it is not a foregone conclusion that the big data trend will impact medicine anytime soon.
Comment · 14 years ago
Privacy policy posted. It's in the footer of symcat.com. Thanks for your patience and let us know if you have any feedback!
Comment · 14 years ago
We observed that we had exact concept matches for over 2/3rds of the symptoms searched on the first try.

To get the rest we are doing two things: 1. adding common queries without matches into our symptom vocab 2. improving the suggested searches algorithm

Comment · 14 years ago
True, but if that alone were the explanation then "shortness of breath, vomiting, and chest pain" would also be higher.

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