[gov3009-l] Applied Statistics Workshop: Francesca Dominici on Wed., February 22

Konstantin Kashin kkashin at fas.harvard.edu
Mon Feb 20 02:15:56 EST 2012


Dear all,

Please join us for the Applied Statistics Workshop (Gov 3009) this
Wednesday, February 22 from 12.00 - 1.30 pm in CGIS Knafel Room 354. Francesca
Dominici <http://www.hsph.harvard.edu/faculty/francesca-dominici/>,
Professor of Biostatistics at the Harvard School of Public Health, will
give a presentation entitled "Bayesian Effect Estimation Accounting for
Adjustment Uncertainty". As always, a light lunch will be provided.

Abstract:

Model-based estimation of the effect of an exposure on an outcome is
> generally sensitive to the choice of which confounding factors are included
> in the model. We propose a new approach, which we call Bayesian Adjustment
> for Confounding (BAC), to estimate the effect on the outcome associated
> with an exposure of interest while accounting for the uncertainty in the
> confounding adjustment. Our approach is based on specifying two models: 1)
> the outcome as a function of the exposure and the potential confounders
> (the outcome model); and 2) the exposure as a function of the potential
> confounders (the exposure model). We consider Bayesian variable selection
> on both models and link the two by introducing a dependence parameter ω
> denoting the prior odds of including a predictor in the outcome model,
> given that the same predictor is in the exposure model. In the absence of
> dependence (ω = 1), BAC reduces to traditional Bayesian Model Averaging
> (BMA). In simulation studies we show that BAC with ω > 1 estimates the
> exposure effect with smaller bias than traditional BMA, and improved
> coverage. We then compare BAC, a recent approach of Crainiceanu et al.
> (2008), and traditional BMA in a time series data set of hospital
> admissions, air pollution levels and weather variables in Nassau, NY for
> the period 1999-2005. Using each approach, we estimate the short-term
> effects of PM2.5 on emergency admissions for cardiovascular diseases,
> accounting for confounding. This application illustrates the potentially
> significant pitfalls of misusing variable selection methods in the context
> of adjustment uncertainty.



An up-to-date schedule for the workshop is available at
http://www.iq.harvard.edu/events/node/1208.


Best,
Konstantin

-- 
Konstantin Kashin
Ph.D. Student in Government
Harvard University

Mobile: 978-844-0538
E-mail: kkashin at fas.harvard.edu
Site: http://www.konstantinkashin.com/<http://people.fas.harvard.edu/%7Ekkashin/>
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