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A weighting approach to causal effects and additive interaction in case-control studies: marginal structural linear odds models

(2011) AMERICAN JOURNAL OF EPIDEMIOLOGY. 174(10). p.1197-1203
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Bioinformatics: from nucleotids to networks (N2N)
Abstract
Estimates of additive interaction from case-control data are often obtained by logistic regression; such models can also be used to adjust for covariates. This approach to estimating additive interaction has come under some criticism because of possible misspecification of the logistic model: If the underlying model is linear, the logistic model will be misspecified. The authors propose an inverse probability of treatment weighting approach to causal effects and additive interaction in case-control studies. Under the assumption of no unmeasured confounding, the approach amounts to fitting a marginal structural linear odds model. The approach allows for the estimation of measures of additive interaction between dichotomous exposures, such as the relative excess risk due to interaction, using case-control data without having to rely on modeling assumptions for the outcome conditional on the exposures and covariates. Rather than using conditional models for the outcome, models are instead specified for the exposures conditional on the covariates. The approach is illustrated by assessing additive interaction between genetic and environmental factors using data from a case-control study.
Keywords
case-control studies, interaction, linear model, structural model, synergism, weighting, SUFFICIENT CAUSE INTERACTIONS, RELATIVE EXCESS RISK, LUNG-CANCER, CONFIDENCE-INTERVALS, SUSCEPTIBILITY LOCUS, SYNERGISM, IDENTIFICATION, EPIDEMIOLOGY

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Citation

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Chicago
VanderWeele, Tyler J, and Stijn Vansteelandt. 2011. “A Weighting Approach to Causal Effects and Additive Interaction in Case-control Studies: Marginal Structural Linear Odds Models.” American Journal of Epidemiology 174 (10): 1197–1203.
APA
VanderWeele, T. J., & Vansteelandt, S. (2011). A weighting approach to causal effects and additive interaction in case-control studies: marginal structural linear odds models. AMERICAN JOURNAL OF EPIDEMIOLOGY, 174(10), 1197–1203.
Vancouver
1.
VanderWeele TJ, Vansteelandt S. A weighting approach to causal effects and additive interaction in case-control studies: marginal structural linear odds models. AMERICAN JOURNAL OF EPIDEMIOLOGY. 2011;174(10):1197–203.
MLA
VanderWeele, Tyler J, and Stijn Vansteelandt. “A Weighting Approach to Causal Effects and Additive Interaction in Case-control Studies: Marginal Structural Linear Odds Models.” AMERICAN JOURNAL OF EPIDEMIOLOGY 174.10 (2011): 1197–1203. Print.
@article{1988858,
  abstract     = {Estimates of additive interaction from case-control data are often obtained by logistic regression; such models can also be used to adjust for covariates. This approach to estimating additive interaction has come under some criticism because of possible misspecification of the logistic model: If the underlying model is linear, the logistic model will be misspecified. The authors propose an inverse probability of treatment weighting approach to causal effects and additive interaction in case-control studies. Under the assumption of no unmeasured confounding, the approach amounts to fitting a marginal structural linear odds model. The approach allows for the estimation of measures of additive interaction between dichotomous exposures, such as the relative excess risk due to interaction, using case-control data without having to rely on modeling assumptions for the outcome conditional on the exposures and covariates. Rather than using conditional models for the outcome, models are instead specified for the exposures conditional on the covariates. The approach is illustrated by assessing additive interaction between genetic and environmental factors using data from a case-control study.},
  author       = {VanderWeele, Tyler J and Vansteelandt, Stijn},
  issn         = {0002-9262},
  journal      = {AMERICAN JOURNAL OF EPIDEMIOLOGY},
  keyword      = {case-control studies,interaction,linear model,structural model,synergism,weighting,SUFFICIENT CAUSE INTERACTIONS,RELATIVE EXCESS RISK,LUNG-CANCER,CONFIDENCE-INTERVALS,SUSCEPTIBILITY LOCUS,SYNERGISM,IDENTIFICATION,EPIDEMIOLOGY},
  language     = {eng},
  number       = {10},
  pages        = {1197--1203},
  title        = {A weighting approach to causal effects and additive interaction in case-control studies: marginal structural linear odds models},
  url          = {http://dx.doi.org/10.1093/aje/kwr334},
  volume       = {174},
  year         = {2011},
}

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