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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\u2010weighting method termed Bayesian post\u2010stratification 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\u2013control population to re\u2010weight the odds ratio estimates obtained from the study. The method is applied to two case\u2013control 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\u2013control studies, our method showed that the odds ratios were only moderately sensitive to selection bias. 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