Open Access
August 2009 Interval Estimation for Messy Observational Data
Paul Gustafson, Sander Greenland
Statist. Sci. 24(3): 328-342 (August 2009). DOI: 10.1214/09-STS305


We review some aspects of Bayesian and frequentist interval estimation, focusing first on their relative strengths and weaknesses when used in “clean” or “textbook” contexts. We then turn attention to observational-data situations which are “messy,” where modeling that acknowledges the limitations of study design and data collection leads to nonidentifiability. We argue, via a series of examples, that Bayesian interval estimation is an attractive way to proceed in this context even for frequentists, because it can be supplied with a diagnostic in the form of a calibration-sensitivity simulation analysis. We illustrate the basis for this approach in a series of theoretical considerations, simulations and an application to a study of silica exposure and lung cancer.


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Paul Gustafson. Sander Greenland. "Interval Estimation for Messy Observational Data." Statist. Sci. 24 (3) 328 - 342, August 2009.


Published: August 2009
First available in Project Euclid: 31 March 2010

zbMATH: 1329.62133
MathSciNet: MR2757434
Digital Object Identifier: 10.1214/09-STS305

Keywords: Bayesian analysis , bias , confounding , epidemiology , hierarchical prior , Identifiability , interval coverage , observational studies

Rights: Copyright © 2009 Institute of Mathematical Statistics

Vol.24 • No. 3 • August 2009
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