Statistical Science

Interval Estimation for Messy Observational Data

Paul Gustafson and Sander Greenland

Full-text: Open access

Abstract

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.

Article information

Source
Statist. Sci., Volume 24, Number 3 (2009), 328-342.

Dates
First available in Project Euclid: 31 March 2010

Permanent link to this document
https://projecteuclid.org/euclid.ss/1270041259

Digital Object Identifier
doi:10.1214/09-STS305

Mathematical Reviews number (MathSciNet)
MR2757434

Zentralblatt MATH identifier
1329.62133

Keywords
Bayesian analysis bias confounding epidemiology hierarchical prior identifiability interval coverage observational studies

Citation

Gustafson, Paul; Greenland, Sander. Interval Estimation for Messy Observational Data. Statist. Sci. 24 (2009), no. 3, 328--342. doi:10.1214/09-STS305. https://projecteuclid.org/euclid.ss/1270041259


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