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June 2010 The sensitivity of linear regression coefficients’ confidence limits to the omission of a confounder
Carrie A. Hosman, Ben B. Hansen, Paul W. Holland
Ann. Appl. Stat. 4(2): 849-870 (June 2010). DOI: 10.1214/09-AOAS315


Omitted variable bias can affect treatment effect estimates obtained from observational data due to the lack of random assignment to treatment groups. Sensitivity analyses adjust these estimates to quantify the impact of potential omitted variables. This paper presents methods of sensitivity analysis to adjust interval estimates of treatment effect—both the point estimate and standard error—obtained using multiple linear regression. Central to our approach is what we term benchmarking, the use of data to establish reference points for speculation about omitted confounders. The method adapts to treatment effects that may differ by subgroup, to scenarios involving omission of multiple variables, and to combinations of covariance adjustment with propensity score stratification. We illustrate it using data from an influential study of health outcomes of patients admitted to critical care.


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Carrie A. Hosman. Ben B. Hansen. Paul W. Holland. "The sensitivity of linear regression coefficients’ confidence limits to the omission of a confounder." Ann. Appl. Stat. 4 (2) 849 - 870, June 2010.


Published: June 2010
First available in Project Euclid: 3 August 2010

zbMATH: 1194.62089
MathSciNet: MR2758424
Digital Object Identifier: 10.1214/09-AOAS315

Keywords: Causal inference , hidden bias , observational study

Rights: Copyright © 2010 Institute of Mathematical Statistics


Vol.4 • No. 2 • June 2010
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