The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Marginal Structural Models as a Tool for Standardization
Abstract
In this article, we show the general relation between standardization methods and marginal structural models. Standardization has been recognized as a method to control confounding and to estimate causal parameters of interest. Because standardization requires stratification by confounders, the sparse-data problem will occur when stratified by many confounders and one then might have an unstable estimator. A new class of causal models called marginal structural models has recently been proposed. In marginal structural models, the parameters are consistently estimated by the inverse-probability-of-treatment weighting method. Marginal structural models give a nonparametric standardization using the total group (exposed and unexposed) as the standard. In epidemiologic analysis, it is also important to know the change in the average risk of the exposed (or the unexposed) subgroup produced by exposure, which corresponds to the exposed (or the unexposed) group as the standard. We propose modifications of the weights in the marginal structural models, which give the nonparametric estimation of standardized parameters. With the proposed weights, we can use the marginal structural models as a useful tool for the nonparametric multivariate standardization.
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Cites
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Cited by
Muller, C. J., & MacLehose, R. F. (2014) · International Journal of Epidemiology · doi:10.1093/ije/dyu029 · Cited by 775
Yu, Y., Arah, O. A., Liew, Z., Cnattingius, S., Olsen, J., Sørensen, H. T., Qin, G., & Li, J. (2019) · BMJ · doi:10.1136/bmj.l6398 · Cited by 305
Pack, A. I., Magalang, U. J., Singh, B., Kuna, S. T., Keenan, B. T., & Maislin, G. (2021) · Sleep · doi:10.1093/sleep/zsaa229 · Cited by 114
Iron status predicts malaria risk in Malawian preschool children
Jonker, F. A. M., Calis, J. C. J., van Hensbroek, M. B., Phiri, K., Geskus, R. B., Brabin, B. J., & Leenstra, T. (2012) · PLoS ONE · doi:10.1371/journal.pone.0042670 · Cited by 77
Understanding Marginal Structural Models for Time-Varying Exposures: Pitfalls and Tips
Shinozaki, T., & Suzuki, E. (2020) · Journal of Epidemiology · doi:10.2188/jea.je20200226 · Cited by 44
Metadata
- Authors
- Sato, T., & Matsuyama, Y.
- Venue
- Epidemiology
- Published
- 2003-11
- Volume
- 14
- Issue
- 6
- Pages
- 680-686
- ISSN
- 1044-3983 (ISSN-L)
- OpenAlex
- W2110201971
- Cited by
- 455
- References
- 16
- FWCI
- 1.63
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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