scholarly journals A zero-augmented generalized gamma regression calibration to adjust for covariate measurement error: A case of an episodically consumed dietary intake

2016 ◽  
Vol 59 (1) ◽  
pp. 94-109 ◽  
Author(s):  
George O. Agogo
Keyword(s):  
Measurement Error ◽  
Dietary Intake ◽  
Regression Calibration ◽  
Generalized Gamma ◽  
Covariate Measurement Error

scholarly journals A comparison of regression calibration, moment reconstruction and imputation for adjusting for covariate measurement error in regression

2008 ◽  
Vol 27 (25) ◽  
pp. 5195-5216 ◽  
Author(s):  
Laurence S. Freedman ◽  
Douglas Midthune ◽  
Raymond J. Carroll ◽  
Victor Kipnis
Keyword(s):  
Measurement Error ◽  
Regression Calibration ◽  
Covariate Measurement Error

scholarly journals Bayesian correction for covariate measurement error: A frequentist evaluation and comparison with regression calibration

2016 ◽  
Vol 27 (6) ◽  
pp. 1695-1708 ◽  
Author(s):  
Jonathan W Bartlett ◽  
Ruth H Keogh
Keyword(s):  
Measurement Error ◽  
Bayesian Approach ◽  
Contributory Factor ◽  
Simulation Studies ◽  
Regression Calibration ◽  
Bayesian Approaches ◽  
Bayesian Analyses ◽  
Health And Nutrition ◽  
Covariate Measurement Error ◽  
The Bayesian Approach

Bayesian approaches for handling covariate measurement error are well established and yet arguably are still relatively little used by researchers. For some this is likely due to unfamiliarity or disagreement with the Bayesian inferential paradigm. For others a contributory factor is the inability of standard statistical packages to perform such Bayesian analyses. In this paper, we first give an overview of the Bayesian approach to handling covariate measurement error, and contrast it with regression calibration, arguably the most commonly adopted approach. We then argue why the Bayesian approach has a number of statistical advantages compared to regression calibration and demonstrate that implementing the Bayesian approach is usually quite feasible for the analyst. Next, we describe the closely related maximum likelihood and multiple imputation approaches and explain why we believe the Bayesian approach to generally be preferable. We then empirically compare the frequentist properties of regression calibration and the Bayesian approach through simulation studies. The flexibility of the Bayesian approach to handle both measurement error and missing data is then illustrated through an analysis of data from the Third National Health and Nutrition Examination Survey.

Propensity score trimming mitigates bias due to covariate measurement error in inverse probability of treatment weighted analyses: A plasmode simulation

2021 ◽  
Vol 40 (9) ◽  
pp. 2101-2112
Author(s):  
Mitchell M. Conover ◽  
Kenneth J. Rothman ◽  
Til Stürmer ◽  
Alan R. Ellis ◽  
Charles Poole ◽  
...  
Keyword(s):  
Measurement Error ◽  
Propensity Score ◽  
Inverse Probability ◽  
Covariate Measurement Error
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