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Two postestimation commands for assessing confounding effects in epidemiological studies

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  • Zhiqiang Wang

    (School of Medicine and School of Population Health, University of Queensland)

Abstract

Confounding is a major issue in observational epidemiological studies. This paper describes two postestimation commands for assessing confounding effects. One command (confall) displays and plots all possible effect estimates against one of p-value, Akaike information criterion, or Bayesian information criterion. This computing-intensive procedure allows researchers to inspect the variability of the effect estimates from various possible models. Another command (chest) uses a stepwise approach to identify variables that have substantially changed the effect estimate. Both commands can be used after most common estimation commands in epidemiological studies, such as logistic regression, conditional logistic regression, Poisson regression, linear regression, and Cox proportional hazards models. Copyright 2007 by StataCorp LP.

Suggested Citation

  • Zhiqiang Wang, 2007. "Two postestimation commands for assessing confounding effects in epidemiological studies," Stata Journal, StataCorp LP, vol. 7(2), pages 183-196, June.
    • Handle: RePEc:tsj:stataj:v:7:y:2007:i:2:p:183-196
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    References listed on IDEAS

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    1. Zhiqiang Wang, 2000. "Assessing confounding effects in epidemiological studies," Stata Technical Bulletin, StataCorp LP, vol. 9(49).
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    Cited by:

    1. Maria Paola Caria & Rino Bellocco & Maria Rosaria Galanti & Nicholas J. Horton, 2011. "The impact of different sources of body mass index assessment on smoking onset: An application of multiple-source information models," Stata Journal, StataCorp LP, vol. 11(3), pages 386-402, September.

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