Article first published online: 10 JAN 2013
Copyright © 2013 John Wiley & Sons, Ltd.
Statistics in Medicine
Volume 32, Issue 15, pages 2555–2570, 10 July 2013
How to Cite
Geneletti, S., Best, N., Toledano, M. B., Elliott, P. and Richardson, S. (2013), Uncovering selection bias in case–control studies using Bayesian post-stratification. Statist. Med., 32: 2555–2570. doi: 10.1002/sim.5722
- Issue published online: 11 JUN 2013
- Article first published online: 10 JAN 2013
- Manuscript Accepted: 9 DEC 2012
- Manuscript Received: 2 AUG 2011
- collider bias;
- combining data;
- conditional independence;
- marginal odds ratio;
- sensitivity analysis
Case–control studies are particularly prone to selection bias, which can affect odds ratio estimation. Approaches to discovering and adjusting for selection bias have been proposed in the literature using graphical and heuristic tools as well as more complex statistical methods. The approach we propose is based on a survey-weighting method termed Bayesian post-stratification and follows from the conditional independences that characterise selection bias. We use our approach to perform a selection bias sensitivity analysis by using ancillary data sources that describe the target case–control population to re-weight the odds ratio estimates obtained from the study. The method is applied to two case–control studies, the first investigating the association between exposure to electromagnetic fields and acute lymphoblastic leukaemia in children and the second investigating the association between maternal occupational exposure to hairspray and a congenital anomaly in male babies called hypospadias. In both case–control studies, our method showed that the odds ratios were only moderately sensitive to selection bias. Copyright © 2013 John Wiley & Sons, Ltd.