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| Harris, Ross J.; Bradburn, Michael J.; Deeks, Jonathan J.; Harbord, Roger M.; Altman, Douglas G.; Sterne, Jonathan A.C.. |
| This article describes updates of the meta-analysis command metan and options that have been added since the command’s original publication (Bradburn, Deeks, and Altman, metan — an alternative meta-analysis command, Stata Technical Bulletin Reprints, vol. 8, pp. 86–100). These include version 9 graphics with flexible display options, the ability to meta-analyze precalculated effect estimates, and the ability to analyze subgroups by using the by() option. Changes to the output, saved variables, and saved results are also described. |
| Tipo: Article |
Palavras-chave: Metan; Meta-analysis; Forest plot; Research Methods/ Statistical Methods. |
| Ano: 2008 |
URL: http://purl.umn.edu/120926 |
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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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| Sterne, Jonathan A.C.; Harbord, Roger M.. |
| Funnel plots are a visual tool for investigating publication and other bias in meta-analysis. They are simple scatterplots of the treatment effects estimated from individual studies (horizontal axis) against a measure of study size (vertical axis). The name “funnel plot” is based on the precision in the estimation of the underlying treatment effect increasing as the sample size of component studies increases. Therefore, in the absence of bias, results from small studies will scatter widely at the bottom of the graph, with the spread narrowing among larger studies. Publication bias (the association of publication probability with the statistical significance of study results) may lead to asymmetrical funnel plots. It is, however, important to realize that... |
| Tipo: Journal Article |
Palavras-chave: Metafunnel; Funnel plots; Meta-analysis; Publication bias; Small-study effects; Research Methods/ Statistical Methods. |
| Ano: 2004 |
URL: http://purl.umn.edu/116233 |
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