Two postestimation commands for assessing confounding effects in epidemiological studies

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.


Issue Date:
2007
Publication Type:
Journal Article
DOI and Other Identifiers:
st0124 (Other)
PURL Identifier:
http://purl.umn.edu/119267
Published in:
Stata Journal, Volume 07, Number 2
Page range:
183-196
Total Pages:
14

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 Record created 2017-04-01, last modified 2017-11-30

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