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    An Investigation of the Significance of Residual Confounding Effect

    199283_117417_An_Investigation_of_the_significance_of_residual_confounding_effect.pdf (1.386Mb)
    Access Status
    Open access
    Authors
    Liang, Wenbin
    Zhao, Y.
    Lee, Andy
    Date
    2014
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Liang, W. and Zhao, Y. and Lee, A. 2014. An Investigation of the Significance of Residual Confounding Effect. BioMed Research International. 2014: Article ID 658056.
    Source Title
    BioMed Research International
    DOI
    10.1155/2014/658056
    ISSN
    2314-6133
    Remarks

    This article is published under the Open Access publishing model and distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0/. Please refer to the licence to obtain terms for any further reuse or distribution of this work.

    URI
    http://hdl.handle.net/20.500.11937/2800
    Collection
    • Curtin Research Publications
    Abstract

    Background: Observational studies are commonly conducted in health research. However, due to their lack of randomization, the estimated associations between the outcome and the exposure can be affected by unmeasured confounding factors. It is important to determine how likely a significant association observed between an outcome variable and a noncausally related exposure may be introduced by residual confounding factors. Methods: A simulation approach is developed based on the sufficient cause model to test the likelihood of significant associations observed between a noncausally related exposure and the outcome. Results: Based on the estimates from all 500 replicates, the association between the exposure and the outcome is found to be significant in 386 (77%) replicates when all confounders (component causes) are controlled for in the model. However, when a subset of real component causes and some noncausal factors are controlled for in the model, the association between exposure and the outcome becomes significant in 487 (97%) replicates. Conclusion: Even when all confounding factors are known and controlled for using conventional multivariate analysis, the observed association between exposure and outcome can still be dominated by residual confounding effects. Therefore, an observed significant association apparently provides limited evidence for a causal relationship.

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