Efficient measurement error correction with spatially misaligned data.

ABSTRACT: Association studies in environmental statistics often involve exposure and outcome data that are misaligned in space. A common strategy is to employ a spatial model such as universal kriging to predict exposures at locations with outcome data and then estimate a regression parameter of interest using the predicted exposures. This results in measurement error because the predicted exposures do not correspond exactly to the true values. We characterize the measurement error by decomposing it into Berkson-like and classical-like components. One correction approach is the parametric bootstrap, which is effective but computationally intensive since it requires solving a nonlinear optimization problem for the exposure model parameters in each bootstrap sample. We propose a less computationally i

SUBMITTER: Szpiro AA 

PROVIDER: S-EPMC3169665 | biostudies-literature | 2011 Oct

REPOSITORIES: biostudies-literature