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On instrumental variables estimation of causal odds ratios

(2011) STATISTICAL SCIENCE. 26(3). p.403-422
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Bioinformatics: from nucleotids to networks (N2N)
Abstract
Inference for causal effects can benefit from the availability of an instrumental variable (IV) which, by definition, is associated with the given exposure, but not with the outcome of interest other than through a causal exposure effect. Estimation methods for instrumental variables are now well established for continuous outcomes, but much less so for dichotomous outcomes. In this article we review IV estimation of so-called conditional causal odds ratios which express the effect of an arbitrary exposure on a dichotomous outcome conditional on the exposure level, instrumental variable and measured covariates. In addition, we propose IV estimators of so-called marginal causal odds ratios which express the effect of an arbitrary exposure on a dichotomous outcome at the population level, and are therefore of greater public health relevance. We explore interconnections between the different estimators and support the results with extensive simulation studies and three applications.
Keywords
Instrumental variable, Marginal effect, Logistic structural mean model., Mendelian randomization, Causal effect, Causal odds ratio, RANDOMIZED CLINICAL-TRIALS, STRUCTURAL MEAN MODELS, MENDELIAN RANDOMIZATION, GENETIC ASSOCIATION, BINARY RESPONSE, NONCOMPLIANCE, INFERENCE, METAANALYSIS, BIAS, IDENTIFICATION

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Citation

Please use this url to cite or link to this publication:

Chicago
Vansteelandt, Stijn, Jack Bowden, Manoochehr Babanezhad, and Els Goetghebeur. 2011. “On Instrumental Variables Estimation of Causal Odds Ratios.” Statistical Science 26 (3): 403–422.
APA
Vansteelandt, S., Bowden, J., Babanezhad, M., & Goetghebeur, E. (2011). On instrumental variables estimation of causal odds ratios. STATISTICAL SCIENCE, 26(3), 403–422.
Vancouver
1.
Vansteelandt S, Bowden J, Babanezhad M, Goetghebeur E. On instrumental variables estimation of causal odds ratios. STATISTICAL SCIENCE. 2011;26(3):403–22.
MLA
Vansteelandt, Stijn, Jack Bowden, Manoochehr Babanezhad, et al. “On Instrumental Variables Estimation of Causal Odds Ratios.” STATISTICAL SCIENCE 26.3 (2011): 403–422. Print.
@article{1935196,
  abstract     = {Inference for causal effects can benefit from the availability of an instrumental variable (IV) which, by definition, is associated with the given exposure, but not with the outcome of interest other than through a causal exposure effect. Estimation methods for instrumental variables are now well established for continuous outcomes, but much less so for dichotomous outcomes. In this article we review IV estimation of so-called conditional causal odds ratios which express the effect of an arbitrary exposure on a dichotomous outcome conditional on the exposure level, instrumental variable and measured covariates. In addition, we propose IV estimators of so-called marginal causal odds ratios which express the effect of an arbitrary exposure on a dichotomous outcome at the population level, and are therefore of greater public health relevance. We explore interconnections between the different estimators and support the results with extensive simulation studies and three applications.},
  author       = {Vansteelandt, Stijn and Bowden, Jack  and Babanezhad, Manoochehr and Goetghebeur, Els},
  issn         = {0883-4237},
  journal      = {STATISTICAL SCIENCE},
  language     = {eng},
  number       = {3},
  pages        = {403--422},
  title        = {On instrumental variables estimation of causal odds ratios},
  url          = {http://dx.doi.org/10.1214/11-STS360},
  volume       = {26},
  year         = {2011},
}

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