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

scholarly journals An imputation-based solution to using mismeasured covariates in propensity score analysis

2015 ◽  
Vol 26 (4) ◽  
pp. 1824-1837 ◽  
Author(s):  
Yenny Webb-Vargas ◽  
Kara E Rudolph ◽  
David Lenis ◽  
Peter Murakami ◽  
Elizabeth A Stuart
Keyword(s):  
Measurement Error ◽  
Propensity Score ◽  
Multiple Imputation ◽  
Propensity Score Analysis ◽  
Calibration Method ◽  
Effect Estimate ◽  
Calibration Data ◽  
Data Set ◽  
External Calibration ◽  
Covariate Measurement Error

Although covariate measurement error is likely the norm rather than the exception, methods for handling covariate measurement error in propensity score methods have not been widely investigated. We consider a multiple imputation-based approach that uses an external calibration sample with information on the true and mismeasured covariates, multiple imputation for external calibration, to correct for the measurement error, and investigate its performance using simulation studies. As expected, using the covariate measured with error leads to bias in the treatment effect estimate. In contrast, the multiple imputation for external calibration method can eliminate almost all the bias. We confirm that the outcome must be used in the imputation process to obtain good results, a finding related to the idea of congenial imputation and analysis in the broader multiple imputation literature. We illustrate the multiple imputation for external calibration approach using a motivating example estimating the effects of living in a disadvantaged neighborhood on mental health and substance use outcomes among adolescents. These results show that estimating the propensity score using covariates measured with error leads to biased estimates of treatment effects, but when a calibration data set is available, multiple imputation for external calibration can be used to help correct for such bias.

scholarly journals Analysis of Gaussian Spatial Models with Covariate Measurement Error

2019 ◽  
Vol 13 (1) ◽  
pp. 77-97
Author(s):  
Vahid Tadayon ◽  
Abdolrahman Rasekh ◽  
◽  
Keyword(s):  
Measurement Error ◽  
Spatial Models ◽  
Covariate Measurement Error
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