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Estimation of Causal Effect Measures in the Presence of Measurement Error in Confounders
- Source :
- Statistics in Biosciences. 10:233-254
- Publication Year :
- 2018
- Publisher :
- Springer Science and Business Media LLC, 2018.
-
Abstract
- The odds ratio, risk ratio, and the risk difference are important measures for assessing comparative effectiveness of available treatment plans in epidemiological studies. Estimation of these measures, however, is often challenged by the presence of error-contaminated confounders. In this article, by adapting two correction methods for measurement error effects applicable to the noncausal context, we propose valid methods which consistently estimate the causal odds ratio, causal risk ratio, and the causal risk difference for settings with error-prone confounders. Furthermore, we develop a bootstrap-based procedure to construct estimators with improved asymptotic efficiency. Numerical studies are conducted to assess the performance of the proposed methods.
- Subjects :
- Statistics and Probability
Estimation
Confounding
Absolute risk reduction
Estimator
Context (language use)
Odds ratio
01 natural sciences
Biochemistry, Genetics and Molecular Biology (miscellaneous)
010104 statistics & probability
03 medical and health sciences
0302 clinical medicine
Causal inference
Relative risk
Statistics
030212 general & internal medicine
0101 mathematics
Mathematics
Subjects
Details
- ISSN :
- 18671772 and 18671764
- Volume :
- 10
- Database :
- OpenAIRE
- Journal :
- Statistics in Biosciences
- Accession number :
- edsair.doi...........7696203120076d3e565b053b18de049a