Med Patient Social Networks Are Better Scientific Institutions

Something like this is useful for the types of data points patients would have no reason to self-deceive over, however I worry that the general tendency for people to make their 'data' fit the stories they've written about themselves in their minds will promote superstitions. For example, a friend of mine is convinced that the aspartame in diet soda caused her rosacea/lupus. She's sent me links to chat-rooms that have blamed aspartame for everything from diabetes to alzheimer's, and it's disturbing to see the kind of positive feed-back loops that are created from anecdotes in which chat members state a clear link exists between symptoms and usage. One says, "I got symptom X after drinking diet soda," and another says, "I have symptom X, it must be from drinking diet soda!" and another says, "Thanks, after reading your comments, I stopped drinking diet soda and symptom X went away!" In spite of chat rooms dedicated to blaming diet soda for every conceivable health problem and the fall of American values, no scientific study to date has shown ANY negative side effect of aspartame even at the upper bounds of current human consumption.

Another example of hysterical positive-feedback would be the proliferation of insane allegations that the MMR vaccine causes autism. I would guess angry parents who wanted to believe MMR caused their child's autism would plot their 'data points' for the onset of their child's symptoms right after vaccination.

A site like this one may allow certain trends to rise out of the noise, but we must not forget the tendency people have to lie to themselves for a convenient story.

You don't want to rely on studies in medical journals because their conclusion-drawing methodologies are haphazard.

I dispute none of this, but so far as I can tell or guess, the main thing powering the superior statistical strength of PatientsLikeMe is the fact that medical researchers have learned to game the system and use complicated ad-hoc frequentist statistics to get whatever answer they want or think they ought to get, and PatientsLikeMe has some standard statistical techniques that they use every time.

Also, I presume, PatientsLikeMe is Bayesian or Bayes-like in that they take all available evidence into account and update incrementally, while every medical experiment is a whole new tiny little frequentist universe.

This is not really an article about PatientsLikeMe being strong, it is an article about the standard statistical methods of academic science being weak and stupid.

I fear this might suffer from Google's problem: once it becomes relied on, the persistent factor which was relied upon gets corrupted.

You can treat it as information, but not as science - it's not protected from malice the same way scientific studies are.

Another great thing about patient social networks is that you can go back and ask the patients questions. Currently, whenever you want to study the correlation between genes and a phenotype, you put together a microarray study, gathering microarray data on a thousand patients, and hiring a doctor to ask each patient "Do you, or have you ever, X, Y, or Z" at a couple hundred bucks per patient. Then you get a DNA sample from each patient, and run it on a microarray, at about $700 per patient.

If you decide you want to look at phenotype W, you do it all over again.

With a social network, patients can post their microarray data, and you can ask them "Do you ever W?" even after the initial study.

Also importantly, you can study things that you would have a hard time getting a grant to study because they're controversial. For instance, correlations between genes and SAT scores.

Last June, I wrote up a brief description for the NSF of a proposed project to use the Affymetrix Genome-Wide Human SNP Array 6.0, which looks for 900,000 SNPs and 900,000 copy number variations. The idea was to provide a website on which patients could upload their own data onto a website, and researchers - or anyone at all - could distribute questionnaires to specific sets of patients. This would let people conduct studies for almost nothing that would currently cost millions of dollars. (A lot of people would argue that you can't ask patients to self-report their symptoms/attributes. I would say, among other things, that I'd rather have self-reporting by 5,000 patients than doctor observations on 20 patients.)

The fact that patients would provide their own data should have been able to get around the strict privacy requirements. Unfortunately, some states, including California, are ahead of me: A patient may not get their own microarray data in CA without going through a doctor.

Also unfortunately, the NSF was so uninterested that they never even responded to my email, which has never happened before.

23andme.com is doing something a little similar. But they don't let you ask phenotype questions of the members, so it's not enough.

The talk was interesting, but I wish he'd spent less time stating that the service is great and more time explaining why it is great. I didn't really get a very good picture of how accurate it is on average or how well it compares to medical studies in general.

Awesome.

Pie in the sky fantasizing:

What I'd really love to see is much cheaper and faster chemical/bio assays, something like a blood glucose monitor that could read out hormone levels, vitamin levels, hematocrit, etc. Something easy enough for millions of people to adopt and commit to on a daily basis. Throw it all into a giant database along with each participant's genome for good measure. The personal insights individuals could gain by comparing their data to the entire database would probably have a much more profound effect on health than a few decades worth of drug development -- of course, the tool would be invaluable for drug development as well.

You don't want to rely on studies in medical journals because their conclusion-drawing methodologies are haphazard.

This is an incredibly strong claim. Mainstream medical research is far from optimal but the medical advancement we've seen in the last 100 years indicates that it's certainly better than random.

Other than that I strongly agree with the article.

The first thing that comes to my hyper paranoid mind is what would happen, for example, if the lithium did seem to work for these patients. Who would then prescribe the lithium, and based on what? If it isn't supported by ordinary studies (let's assume it isn't) and it's not even remotely standard of care, then there's going to be some trouble.

So I'm a progressive doctor, and I look this data over and I say to give it a try; and the patient dies of lithium toxicity. Seems like they have a good case against me.

I do think it's a defensible maneuver (they could still sue me, though) but my worry is more about the very difficult position this puts everyone in. I'm not against this type of research, just wondering aloud.

I'd speculate that this is better suited for surveillance, or for identifying which subgroups benefit more or less from a treatment. Everyone's going to hate this idea, but I think it's quite realistic: it would be great if doctors in general uploaded such data to a common site, especially about illnesses for which novel treatments are lacking. Pharma could/would help cover the costs, of this I am sure.

Free the data!

The reporting engine they have created is impressive but it always better to have more people looking at the data. Who knows what mashups hackers on the side would make?

I applaud the idea of getting a large set medical data together though crowd sourcing. I just wish anyone could run there own statistics on the data.

This is great. But what's FRS?

I'm going to forward this to my mom.

The "people" in the quoted bit are correct. This is not science; this is statistical analysis.

It is possible that an individual would be better served by this social network, though I have generally agreed that a physician who treats himself has a fool for a patient, and the more so for a layman who neglects to consult competent medical authorities. These social networks certainly cannot take the place of original research; they rely on existing observed trends.

If this gets promoted, it will make an interesting case study in what it takes for a completely off-topic link reference to get promoted.