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The key sentence in VanderWeele's paper is this: "Here, I use the
standard errors and confidence intervals (CIs) reported by Christakis
and Fowler in their analyses". Recall that the CIs (and indeed the
estimates themselves) are meaningless since the models contradict the
data and the conclusions and in fact imply no effect. This was discussed
in prior posts.
Who wants to know how many angels can dance on the head of a pin (to
continue the medieval theme begun by Snijders)? or how robust are the
confidence fairies (to appropriate a phrase from Krugman)?
On Sun, 6 Nov 2011, Barry Wellman wrote:
> ***** To join INSNA, visit http://www.insna.org *****
> Thanks to Brian Keegan for tweeting the link
> Barry Wellman
> S.D. Clark Professor of Sociology, FRSC NetLab Director
> Department of Sociology 725 Spadina Avenue, Room 388
> University of Toronto Toronto Canada M5S 2J4 twitter:barrywellman
> http://www.chass.utoronto.ca/~wellman fax:+1-416-978-3963
> Updating history: http://chass.utoronto.ca/oldnew/cybertimes.php
> App Stats: VanderWeele on "Sensitivity Analysis for Contagion Effects in
> Social Networks"
> We hope you can join us this Wednesday, November 9, 2011 for the Applied
> Statistics Workshop. Tyler VanderWeele, Associate Professor of Epidemiology
> at the Harvard School of Public Health, will give a presentation entitled
> "Sensitivity Analysis for Contagion Effects in Social Networks". A light
> lunch will be served at 12 pm and the talk will begin at 12.15.
> "Sensitivity Analysis for Contagion Effects in Social Networks"
> Tyler VanderWeele
> Harvard School of Public Health
> CGIS K354 (1737 Cambridge St.)
> Wednesday, November 9th, 2011 12.00 pm
> The paper is available here.
> Analyses of social network data have suggested that obesity, smoking,
> happiness, and loneliness all travel through social networks. Individuals
> exert ''contagion effects'' on one another through social ties and
> association. These analyses have come under critique because of the
> possibility that homophily from unmeasured factors may explain these
> statistical associations and because similar findings can be obtained when
> the same methodology is applied to height, acne, and headaches, for which the
> conclusion of contagion effects seems somewhat less plausible. The author
> uses sensitivity analysis techniques to assess the extent to which supposed
> contagion effects for obesity, smoking, happiness, and loneliness might be
> explained away by homophily or confounding and the extent to which the
> critique using analysis of data on height, acne, and headaches is relevant.
> Sensitivity analyses suggest that contagion effects for obesity and smoking
> cessation are reasonably robust to possible latent homophily or environmental
> confounding; those for happiness and loneliness are somewhat less so.
> Supposed effects for height, acne, and headaches are all easily explained
> away by latent homophily and confounding. The methodology that has been used
> in past studies for contagion effects in social networks, when used in
> conjunction with sensitivity analysis, may prove useful in establishing
> social influence for various behaviors and states. The sensitivity analysis
> approach can be used to address the critique of latent homophily as a
> possible explanation of associations interpreted as contagion effects.
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