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Selecting Confounders from Covariates

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  • Gillian M. Raab

Abstract

Most analyses of observational studies to investigate the relationship between a risk factor and an outcome use some data‐dependent method to select a subset of the available covariates to include as confounders. This paper evaluates methods which can be used to make this selection in linear models. The methods are applied to the selection of covariates in a study of the effect of lead in the blood on children's ability and are evaluated by a simulation study on multivariate normal data. None of the methods can be guaranteed to give satisfactory results in all cases, and all the methods can result in misleading inferences. Recommendations for the design and analysis of observational studies are made in the light of these results.

Suggested Citation

  • Gillian M. Raab, 1994. "Selecting Confounders from Covariates," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 157(2), pages 271-283, March.
    • Handle: RePEc:bla:jorssa:v:157:y:1994:i:2:p:271-283
    DOI: 10.2307/2983362
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    Cited by:

    1. Lian, Ie-Bin, 2003. "Reducing over-dispersion by generalized degree of freedom and propensity score," Computational Statistics & Data Analysis, Elsevier, vol. 43(2), pages 197-214, June.

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