In econometrics, or applied economics, a “placebo test” is not a comparison of a drug to a sugar pill. Rather, it’s a sort of conceptual placebo, in which you repeat your analysis using a different dataset, or a different part … Continue reading
One of my favorite points arose in the seminar today. A regression was fit to data from the 2000 Census and the 1990 Census, a question involving literacy of people born in the 1958-1963 period. The result was statistically significant … Continue reading
I sent Howard Wainer the causal quartets paper and he wrote that it reminded him of a theory he had about placebos: I have always believed (without supporting evidence) that often a substantial amount of what is called a placebo … Continue reading
July: The Ponzi threshold and the Armstrong principle Flaws in stupid horrible algorithm revealed because it made numerical predictions PNAS forgets basic principles of game theory, thus dooming thousands of Bothans to the fate of Alderaan Tutorial: The practical application … Continue reading
David Allison points us to this article by Bryan McComb, Alexis Frazier-Wood, John Dawson, and himself, “Drawing conclusions from within-group comparisons and selected subsets of data leads to unsubstantiated conclusions.” It’s a letter to the editor for the Australian and … Continue reading
Here’s what we got scheduled for ya: I’m with Errol: On flypaper, photography, science, and storytelling Politically extreme yet vital to the nation How does probabilistic computation differ in physics and statistics? “Each computer run would last 1,000-2,000 hours, and, … Continue reading
Fabio Rojas writes: After reading the Fowler/Christakis paper on networks and obesity, a student asked why it was that friends had a stronger influence on spouses. In other words, if we believe the F&C paper, they report that your friends … Continue reading
I was just thinking about this because we seem to be circling around the same few topics over and over (while occasionally slipping in some new statistical ideas):
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 lines of reporting, along with general statistical thoughts.
We last encountered Christakis-Fowler last April, when Dave Johns reported on some criticisms coming from economists Jason Fletcher and Ethan Cohen-Cole and mathematician Russell Lyons.
Lyons’s paper was recently published under the title, The Spread of Evidence-Poor Medicine via Flawed Social-Network Analysis. Lyons has a pretty aggressive tone–he starts the abstract with the phrase “chronic widespread misuse of statistics” and it gets worse from there–and he’s a bit rougher on Christakis and Fowler than I would be, but this shouldn’t stop us from evaluating his statistical arguments. Here are my thoughts: Continue reading