Journal article
Introduction to causal diagrams for confounder selection
EJ Williamson, Z Aitken, J Lawrie, SC Dharmage, JA Burgess, AB Forbes
Respirology | WILEY-BLACKWELL | Published : 2014
DOI: 10.1111/resp.12238
Abstract
In respiratory health research, interest often lies in estimating the effect of an exposure on a health outcome. If randomization of the exposure of interest is not possible, estimating its effect is typically complicated by confounding bias. This can often be dealt with by controlling for the variables causing the confounding, if measured, in the statistical analysis. Common statistical methods used to achieve this include multivariable regression models adjusting for selected confounding variables or stratification on those variables. Therefore, a key question is which measured variables need to be controlled for in order to remove confounding. An approach to confounder-selection based on ..
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Funding Acknowledgements
We thank Associate Professor Julie Simpson from the Melbourne School of Population and Global Health, The University of Melbourne and Professor Michael Abramson from the School of Public Health and Preventive Medicine, Monash University for useful comments on the manuscript, and Professor Dallas English from the Melbourne School of Population and Global Health, The University of Melbourne for years of discussion and debate about causal diagrams in epidemiology. We thank the TAHS Steering Committee for providing us with a random subset of the data from the TAHS cohort that was funded by the National Health and Medical Research Council, Australia, ID#299901. This work was supported under a National Health and Medical Research Council Centre of Research Excellence grant, ID#1035261, to the Victorian Centre for Biostatistics (ViCBiostat).