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Doubly robust conditional logistic regression

(2019) STATISTICS IN MEDICINE. 38(23). p.4749-4760
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Abstract
Epidemiologic research often aims to estimate the association between a binary exposure and a binary outcome, while adjusting for a set of covariates (eg, confounders). When data are clustered, as in, for instance, matched case-control studies and co-twin-control studies, it is common to use conditional logistic regression. In this model, all cluster-constant covariates are absorbed into a cluster-specific intercept, whereas cluster-varying covariates are adjusted for by explicitly adding these as explanatory variables to the model. In this paper, we propose a doubly robust estimator of the exposure-outcome odds ratio in conditional logistic regression models. This estimator protects against bias in the odds ratio estimator due to misspecification of the part of the model that contains the cluster-varying covariates. The doubly robust estimator uses two conditional logistic regression models for the odds ratio, one prospective and one retrospective, and is consistent for the exposure-outcome odds ratio if at least one of these models is correctly specified, not necessarily both. We demonstrate the properties of the proposed method by simulations and by re-analyzing a publicly available dataset from a matched case-control study on induced abortion and infertility.
Keywords
conditional logistic regression, conditional maximum likelihood, doubly robust estimation, CAUSAL INFERENCE

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MLA
Zetterqvist, Johan, et al. “Doubly Robust Conditional Logistic Regression.” STATISTICS IN MEDICINE, vol. 38, no. 23, 2019, pp. 4749–60.
APA
Zetterqvist, J., Vermeulen, K., Vansteelandt, S., & Sjölander, A. (2019). Doubly robust conditional logistic regression. STATISTICS IN MEDICINE, 38(23), 4749–4760.
Chicago author-date
Zetterqvist, Johan, Karel Vermeulen, Stijn Vansteelandt, and Arvid Sjölander. 2019. “Doubly Robust Conditional Logistic Regression.” STATISTICS IN MEDICINE 38 (23): 4749–60.
Chicago author-date (all authors)
Zetterqvist, Johan, Karel Vermeulen, Stijn Vansteelandt, and Arvid Sjölander. 2019. “Doubly Robust Conditional Logistic Regression.” STATISTICS IN MEDICINE 38 (23): 4749–4760.
Vancouver
1.
Zetterqvist J, Vermeulen K, Vansteelandt S, Sjölander A. Doubly robust conditional logistic regression. STATISTICS IN MEDICINE. 2019;38(23):4749–60.
IEEE
[1]
J. Zetterqvist, K. Vermeulen, S. Vansteelandt, and A. Sjölander, “Doubly robust conditional logistic regression,” STATISTICS IN MEDICINE, vol. 38, no. 23, pp. 4749–4760, 2019.
@article{8624244,
  abstract     = {Epidemiologic research often aims to estimate the association between a binary exposure and a binary outcome, while adjusting for a set of covariates (eg, confounders). When data are clustered, as in, for instance, matched case-control studies and co-twin-control studies, it is common to use conditional logistic regression. In this model, all cluster-constant covariates are absorbed into a cluster-specific intercept, whereas cluster-varying covariates are adjusted for by explicitly adding these as explanatory variables to the model. In this paper, we propose a doubly robust estimator of the exposure-outcome odds ratio in conditional logistic regression models. This estimator protects against bias in the odds ratio estimator due to misspecification of the part of the model that contains the cluster-varying covariates. The doubly robust estimator uses two conditional logistic regression models for the odds ratio, one prospective and one retrospective, and is consistent for the exposure-outcome odds ratio if at least one of these models is correctly specified, not necessarily both. We demonstrate the properties of the proposed method by simulations and by re-analyzing a publicly available dataset from a matched case-control study on induced abortion and infertility.},
  author       = {Zetterqvist, Johan and Vermeulen, Karel and Vansteelandt, Stijn and Sjölander, Arvid},
  issn         = {0277-6715},
  journal      = {STATISTICS IN MEDICINE},
  keywords     = {conditional logistic regression,conditional maximum likelihood,doubly robust estimation,CAUSAL INFERENCE},
  language     = {eng},
  number       = {23},
  pages        = {4749--4760},
  title        = {Doubly robust conditional logistic regression},
  url          = {http://dx.doi.org/10.1002/sim.8332},
  volume       = {38},
  year         = {2019},
}

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