andrew_gelman_stats andrew_gelman_stats-2011 andrew_gelman_stats-2011-757 knowledge-graph by maker-knowledge-mining
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Introduction: Nicholas Christakis and James Fowler are famous for finding that obesity is contagious. Their claims, which have been received with both respect and skepticism (perhaps we need a new word for this: “respecticism”?) are based on analysis of data from the Framingham heart study, a large longitudinal public-health study that happened to have some social network data (for the odd reason that each participant was asked to provide the name of a friend who could help the researchers locate them if they were to move away during the study period. The short story is that if your close contact became obese, you were likely to become obese also. The long story is a debate about the reliability of this finding (that is, can it be explained by measurement error and sampling variability) and its causal implications. This sort of study is in my wheelhouse, as it were, but I have never looked at the Christakis-Fowler work in detail. Thus, my previous and current comments are more along the line
sentIndex sentText sentNum sentScore
1 Lyons recognizes this, writing, “while the world may indeed work as C&F; say, their studies do not provide evidence to support such claims. [sent-16, score-0.217]
2 ” I wouldn’t go quite so far as to say they don’t provide evidence, but it seems fair to say they don’t provide convincing or compelling evidence. [sent-17, score-0.221]
3 In debates about empirical social science, there is often a tendency to simply accept descriptive claims and move straight to the arguments about their implications. [sent-21, score-0.216]
4 But as I’ve learned in my own research, often the descriptive claims themselves should be disputed. [sent-22, score-0.216]
5 ) So I’d like to separate Lyons’s criticism of the descriptive inferences and the causal implications. [sent-24, score-0.246]
6 The descriptive criticism is that some of Christakis and Fowler’s observed differences are not statistically significant, thus there is some doubt about generalization to the larger population, it could all just be patterns in random noise. [sent-25, score-0.419]
7 The causal criticism is that, if the descriptive patterns do generalize, they could be explained in other ways than contagion. [sent-26, score-0.306]
8 In particular, he notes on page 6 that the difference between significant and non-significant is not itself statistically significant , a point that should be familiar to regular readers of this space. [sent-29, score-0.234]
9 Lyons goes a bit over the top in the conclusion of his article, slamming observational studies and modeling in general. [sent-34, score-0.291]
10 But statistical modeling is important and useful in many many areas of science and engineering. [sent-35, score-0.22]
11 Bob writes the following about models in computational linguistics: Google translate is heavily model based, being derived from IBM’s original statistical translation models. [sent-40, score-0.337]
12 Ad placement is also heavily model based, and works at least as far as Google’s revenue is concerned. [sent-41, score-0.245]
13 All of the speech recognition in everything from call centers to the desktop is heavily model based, and works pretty well judging by the numbers of people using it. [sent-42, score-0.247]
14 Even seemingly slam-dunk models such as simple random sampling are not true with real surveys, nor are randomization models actually true with experiments on real people. [sent-50, score-0.242]
15 ) I think one should step back before slamming any research just cos it’s observational and model based. [sent-53, score-0.2]
16 Once you have good data, you might very well want to model to learn important things. [sent-67, score-0.202]
17 Which involved lots of work, lots of interaction between the science and the data, and lots of checking. [sent-71, score-0.195]
18 It’s easy to write a sentence like, “viewing observational data through the lens of statistical modeling produces new biases, generally unknown and mostly unacknowledged, lurking in mathematical thickets. [sent-78, score-0.316]
19 After all, even if the Framingham results were unambiguously statistically significant, robust to reasonable models of measurement error, and had a clean identification strategy–even then, it’s just one group of people. [sent-84, score-0.27]
20 I conveyed point 5 above to Lyons and he responded that he respected models too but was concerned with models that cannot be tested. [sent-89, score-0.242]
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Introduction: Russ Lyons points us to a discussion in Statistics in Medicine of the famous claims by Christakis and Fowler on the contagion of obesity etc. James O’Malley and Christakis and Fowler present the positive case. Andrew Thomas and Tyler VanderWeele present constructive criticism. Christakis and Fowler reply . Coincidentally, a couple weeks ago an epidemiologist was explaining to me the differences between the Framingham Heart Study and the Nurses Health Study and why Framingham got the postmenopausal supplement risks right while Nurses got it wrong. P.S. The journal issue also includes a comment on “A distribution-free test of constant mean in linear mixed effects models.” Wow! I had no idea people still did this sort of thing. How horrible. But I guess that’s what half-life is all about. These ideas last forever, they just become less and less relevant to people.
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Introduction: AT discusses [link broken; see P.P.S. below] a new paper of his that casts doubt on the robustness of the controversial Christakis and Fowler papers. AT writes that he ran some simulations of contagion on social networks and found that (a) in a simple model assuming the contagion of the sort hypothesized by Christakis and Fowler, their procedure would indeed give the sorts of estimates they found in their papers, but (b) in another simple model assuming a different sort of contagion, the C&F; estimation would give indistinguishable estimates. Thus, if you believe AT’s simulation model, C&F;’s procedure cannot statistically distinguish between two sorts of contagion (directional and simultaneous). I have not looked at AT’s paper so I can’t fully comment, but I don’t fully understand his method for simulating network connections. AT uses what he calls a “rewiring” model. This makes sense: as time progresses, we make new friends and lose old ones—but I am confused by the details
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