Uncovering selection bias in case-control studies using Bayesian post-stratification
| Autor: | Paul Elliott, Mireille B. Toledano, Nicky Best, Sara Geneletti, Sylvia Richardson |
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| Rok vydání: | 2013 |
| Předmět: |
Adult
Male Statistics and Probability Epidemiology Computer science media_common.quotation_subject Bayesian probability Population Hair Preparations Biostatistics Electromagnetic Fields Pregnancy Occupational Exposure Statistics Odds Ratio Econometrics Humans Information bias Child education Selection Bias media_common Estimation Selection bias Hypospadias education.field_of_study Heuristic Infant Newborn Case-control study Bayes Theorem Odds ratio Precursor Cell Lymphoblastic Leukemia-Lymphoma Maternal Exposure Case-Control Studies Female |
| Zdroj: | Statistics in Medicine. 32:2555-2570 |
| ISSN: | 0277-6715 |
| DOI: | 10.1002/sim.5722 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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