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CORRECTING INSTRUMENTAL VARIABLES ESTIMATORS FOR SYSTEMATIC MEASUREMENT ERROR

(2009) STATISTICA SINICA. 19(3). p.1223-1246
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Abstract
Instrumental variables (IV) estimators are well established in a broad range of Fields to correct for measurement error on exposure. In a distinct prominent stream of research, IV's are becoming increasingly popular for estimating causal effects of exposure on outcome since they allow for unmeasured confounders which are hard to avoid. Because many causal questions emerge from data which suffer severe measurement error problems, we combine both IV approaches in this article to correct IV-based causal effect estimators in linear (structural mean) models for possibly systematic measurement error on the exposure. The estimators rely on the presence of a baseline measurement that is associated with the observed exposure and known not to modify the target effect. Simulation studies and the analysis of a small blood pressure reduction trial (n = 105) with treatment noncompliance confirm the adequate performance of our estimators in finite samples. Our results also demonstrate that incorporating limited prior knowledge about a weakly identified parameter (such as the error mean) in a frequentist analysis can yield substantial improvements.
Keywords
CAUSAL INFERENCE, EPIDEMIOLOGISTS, STRUCTURAL MEAN MODELS, RANDOMIZED CLINICAL-TRIALS, MENDELIAN RANDOMIZATION, POTENTIAL OUTCOMES, NUISANCE PARAMETER, NONCOMPLIANCE

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Citation

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Chicago
Vansteelandt, Stijn, Manoochehr Babanezhad, and Els Goetghebeur. 2009. “Correcting Instrumental Variables Estimators for Systematic Measurement Error.” Statistica Sinica 19 (3): 1223–1246.
APA
Vansteelandt, S., Babanezhad, M., & Goetghebeur, E. (2009). CORRECTING INSTRUMENTAL VARIABLES ESTIMATORS FOR SYSTEMATIC MEASUREMENT ERROR. STATISTICA SINICA, 19(3), 1223–1246.
Vancouver
1.
Vansteelandt S, Babanezhad M, Goetghebeur E. CORRECTING INSTRUMENTAL VARIABLES ESTIMATORS FOR SYSTEMATIC MEASUREMENT ERROR. STATISTICA SINICA. TAIPEI: STATISTICA SINICA; 2009;19(3):1223–46.
MLA
Vansteelandt, Stijn, Manoochehr Babanezhad, and Els Goetghebeur. “Correcting Instrumental Variables Estimators for Systematic Measurement Error.” STATISTICA SINICA 19.3 (2009): 1223–1246. Print.
@article{791150,
  abstract     = {Instrumental variables (IV) estimators are well established in a broad range of Fields to correct for measurement error on exposure. In a distinct prominent stream of research, IV's are becoming increasingly popular for estimating causal effects of exposure on outcome since they allow for unmeasured confounders which are hard to avoid. Because many causal questions emerge from data which suffer severe measurement error problems, we combine both IV approaches in this article to correct IV-based causal effect estimators in linear (structural mean) models for possibly systematic measurement error on the exposure. The estimators rely on the presence of a baseline measurement that is associated with the observed exposure and known not to modify the target effect. Simulation studies and the analysis of a small blood pressure reduction trial (n = 105) with treatment noncompliance confirm the adequate performance of our estimators in finite samples. Our results also demonstrate that incorporating limited prior knowledge about a weakly identified parameter (such as the error mean) in a frequentist analysis can yield substantial improvements.},
  author       = {Vansteelandt, Stijn and Babanezhad, Manoochehr and Goetghebeur, Els},
  issn         = {1017-0405},
  journal      = {STATISTICA SINICA},
  language     = {eng},
  number       = {3},
  pages        = {1223--1246},
  publisher    = {STATISTICA SINICA},
  title        = {CORRECTING INSTRUMENTAL VARIABLES ESTIMATORS FOR SYSTEMATIC MEASUREMENT ERROR},
  volume       = {19},
  year         = {2009},
}

Web of Science
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