Not contagious after all?

(via Decision Science News) Ouch! A new paper titled “The Spread of Evidence-Poor Medicine via Flawed Social-Network Analysis” (published here and available in manuscript format on arXiv) has come out arguing very strongly against the conclusions drawn by Christakis and Fowler in a series of papers where they put forward the idea that things like obesity and smoking can be transmitted through social networks; a kind of “social contagion.” I blogged about these ideas a while back after both Wired and the New York Times had published articles on them. The title (harsh!) and the abstract speaks for itself:

The chronic widespread misuse of statistics is usually inadvertent, not intentional. We find cautionary examples in a series of recent papers by Christakis and Fowler that advance statistical arguments for the transmission via social networks of various personal characteristics, including obesity, smoking cessation, happiness, and loneliness. Those papers also assert that such influence extends to three degrees of separation in social networks. We shall show that these conclusions do not follow from Christakis and Fowler’s statistical analyses. In fact, their studies even provide some evidence against the existence of such transmission. The errors that we expose arose, in part, because the assumptions behind the statistical procedures used were insufficiently examined, not only by the authors, but also by the reviewers. Our examples are instructive because the practitioners are highly reputed, their results have received enormous popular attention, and the journals that published their studies are among the most respected in the world. An educational bonus emerges from the difficulty we report in getting our critique published. We discuss the relevance of this episode to understanding statistical literacy and the role of scientific review, as well as to reforming statistics education.

Cosma Shalizi has co-authored another paper (available here) which makes a similar point in a much more, let’s say, polite way. My impression is that Shalizi is both sharp and trustworthy (I’ve learned a lot about statistics from his blog) so I’m inclined to think he is on to something.

… but does it work?

Upon learning about the possible future of medicine, including self-tracking and social networks for patients, you might wonder whether these things really work or if they are just nice ideas. Well, now there are at least some indications that they are useful.

The Decision Tree quotes a report from the the Kaiser Permanente Center for Health Research which says that people who kept track of how much food they ate lost twice as much weight as people who didn’t in a study about weight loss. As a press release puts it, “It seems that the simple act of writing down what you eat encourages people to consume fewer calories.” The study was published in the American Journal of Preventive Medicine in August. However, the effect is much more powerful if people exercise and self-track together, in line with ideas about “social contagion” that I have discussed before.

(By the way, the last several posts at The Decision Tree about the Health2.0 conference and the Kaiser Permanente’s HealthCamp “unconference” make for interesting reading about everything that is brewing in this field.)

What about “social medicine”? A couple of weeks ago, Alexandra Carmichael from CureTogether gave a talk at the Mayo Clinic and revealed that the company has achieved its first statistically significant finding based solely on self-reported data from users of their site. According to these “patient-generated data”, people with infertility are twice as likely to have asthma. This has also been found before in controlled clinical studies. It’s a sign that aggregated self-reported data has the potential to uncover many known and unknown correlations.

On the theme of patient data, there is a new piece, Owning Your Health Information – An Inalienable Right by Leslie Saxon, which is worth a read.

And a final reading tip: a Nature opinion piece (with Craig Venter as one of the authors) called An agenda for personalized medicine. It discusses how disease risk assessments obtained from two direct-to-consumer genetic testing companies can vary quite a lot in some cases. The authors suggest a number of “best practices” to improve disease risk predictions.

Everything is contagious

I’ve been putting off writing a lengthy blog post on this topic for a while, but today I found that both the New York Times and Wired have new articles out on the same subject (see below), so I might as well point to them while at the same time offering some of my half-baked thoughts.

A couple of weeks back, I was listening to a podcast from the SmartData Collective podcast series where a guy named Korhan Yunak talked about predicting if and when a customer would cancel their mobile phone subscription and switch to another provider. All kinds of demographic information, behavioural data and other things have been used to try to extract features that predict such switches. Yunak explained that recent research had found that essentially, subscription switches propagate through social networks. What does that mean exactly?

Phone companies can construct a customer network by collecting “connections” between customers (for example, by linking everyone that has called or texted each other). By simply looking at a customer’s network neighborhood – their direct connections (often friends) and perhaps the friends of friends – the companies can get a huge boost in their predictive accuracy  (I’ve forgotten the exact number and metric, but it was a major improvement) .

Now, it is not surprising in itself that people talk to each other and influence each other in different ways, but it was surprising to me that the effect was so strong. It made me think of earlier published work which showed that obesity, happiness and smoking are all “socially contagious” in the sense that they seem to spread through social networks.

As I mentioned above, there is a new Wired article by Jonah Lehrer which talks about these things and has nice visualizations of them as well. There’s also a New York Times article on the same theme by Clive Thompson, but I haven’t read it because of the paywall.

These findings, of course, suggest a new kind of “network marketing” (the “old kind” also goes by the name of multi-level marketing). The idea is that you can use information about a customer’s friends’ preferences and shopping behavior to construct more precise targeted ads and other marketing strategies. Companies based around such ideas include Media6°, which “…connects a brand’s existing customers with user segments composed entirely of consumers who are interwoven via the social graph.” Another company, 33Across, “…uses previously untapped social data sources, in combination with advanced social network algorithms, to create unique and scalable audience segments.” Both companies do this by capturing data from social network sites on the web, according to this article.