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| Fewell, Zoe; Hernan, Miguel A.; Wolfe, Frederick; Tilling, Kate; Choi, Hyon; Sterne, Jonathan A.C.. |
| Longitudinal studies in which exposures, confounders, and outcomes are measured repeatedly over time have the potential to allow causal inferences about the effects of exposure on outcome. There is particular interest in estimating the causal effects of medical treatments (or other interventions) in circumstances in which a randomized controlled trial is difficult or impossible. However, standard methods for estimating exposure effects in longitudinal studies are biased in the presence of time-dependent confounders affected by prior treatment. This article describes the use of marginal structural models (described by Robins, Hernán, and Brumback [2000]) to estimate exposure or treatment effects in the presence of time-dependent confounders affected by... |
| Tipo: Journal Article |
Palavras-chave: Marginal structural models; Causal models; Weighted regression; Survival analysis; Logistic regression; Confounding; Research Methods/ Statistical Methods. |
| Ano: 2004 |
URL: http://purl.umn.edu/116267 |
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| Wang, Zhiqiang. |
| 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,... |
| Tipo: Article |
Palavras-chave: Confall; Confgr; Chest; Epidemiological methods; Confounding; All possible effects; Change in estimate; Research Methods/ Statistical Methods. |
| Ano: 2007 |
URL: http://purl.umn.edu/119267 |
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