Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2014 (2014), Article ID 872435, 8 pages
Research Article

A Proxy Outcome Approach for Causal Effect in Observational Studies: A Simulation Study

1National Drug Research Institute, Health Science, Curtin University, G.P.O. Box U 1987, Perth, WA 6845, Australia
2Northern Territory Department of Health, Darwin, NT 0800, Australia
3School of Public Health, Health Science, Curtin University, Perth, WA 6845, Australia

Received 1 November 2013; Revised 3 January 2014; Accepted 3 January 2014; Published 18 February 2014

Academic Editor: Abdelwahab Omri

Copyright © 2014 Wenbin Liang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Background. Known and unknown/unmeasured risk factors are the main sources of confounding effects in observational studies and can lead to false observations of elevated protective or hazardous effects. In this study, we investigate an alternative approach of analysis that is operated on field-specific knowledge rather than pure statistical assumptions. Method. The proposed approach introduces a proxy outcome into the estimation system. A proxy outcome possesses the following characteristics: (i) the exposure of interest is not a cause for the proxy outcome; (ii) causes of the proxy outcome and the study outcome are subsets of a collection of correlated variables. Based on these two conditions, the confounding-effect-driven association between the exposure and proxy outcome can then be measured and used as a proxy estimate for the effects of unknown/unmeasured confounders on the outcome of interest. Performance of this approach is tested by a simulation study, whereby 500 different scenarios are generated, with the causal factors of a proxy outcome and a study outcome being partly overlapped under low-to-moderate correlations. Results. The simulation results demonstrate that the conventional approach only led to a correct conclusion in 21% of the 500 scenarios, as compared to 72.2% for the alternative approach. Conclusion. The proposed method can be applied in observational studies in social science and health research that evaluates the health impact of behaviour and mental health problems.