Paciorek on 'Spatial scale and bias in regression models with spatial confounding'. 

 The Applied Statistics Workshop returns tomorrow (11/14) with Chris Paciorek, Department of Biostatistics in the School of Public Health, presenting this work on , 'Spatial scale and bias in regression models with spatial confounding'.  Chris provided the following abstract for his talk: 

 When unmeasured confounders vary spatially, a common technique in regression modeling, including spatial epidemiology applications, is to try to account for the unmeasured confounding by modeling residual spatial correlation. The intuition is that modeling the spatial structure will remove large scale variation and allow one to estimate the effect of the covariate of interest based on variation in the outcome isolated at smaller scales.  Previous work in the temporal setting indicates that when the variable of interest has an uncorrelated component then such an approach can minimize bias. Here I consider the situation that the variable of interest varies at multiple spatial scales but may not have a non-spatial component. I develop a framework for understanding bias using a simple generalized least squares model with data collected at point locations and fixed and known spatial scales. I show that bias is substantial even when the scales are known, unless the variable of inte rest has an unconfounded component that varies at a finer spatial scale than the confounder. Using simulation I consider the effect of estimating the scale of the residual spatial correlation on bias, showing that bias is similar when variance and scale parameters are estimated to when they are known.  I discuss extensions to data aggregated into areal units and to the setting of measurement error in the covariate of interest. 

 As always, the workshop will convene at 12 noon, in room N-354, CGIS-Knafel.  And a light lunch will be served.   

 Hope you all can make it!