Abstract：
Observational studies are rarely representative of their target population because there are known and unknown factors that affect an individual\'s choice to participate (the selection mechanism). Selection can cause bias in a given analysis if the outcome is related to selection (conditional on the other variables in the model). Detecting and adjusting for selection bias in practice typically requires access to data on nonselected individuals. Here, we propose methods to detect selection bias in genetic studies by comparing correlations among genetic variants in the selected sample to those expected under no selection. We examine the use of four hypothesis tests to identify induced associations between genetic variants in the selected sample. We evaluate these approaches in Monte Carlo simulations. Finally, we use these approaches in an applied example using data from the UK Biobank (UKBB). The proposed tests suggested an association between alcohol consumption and selection into UKBB. Hence, UKBB analyses with alcohol consumption as the exposure or outcome may be biased by this selection.
摘要：
观察性研究很少能代表其目标人群，因为有已知和未知的因素会影响个体的参与选择（选择机制）。如果结果与选择有关（以模型中的其他变量为条件），则选择会导致给定分析中的偏差。在实践中检测和调整选择偏差通常需要访问非选择个体的数据。这里,我们提出了在遗传研究中检测选择偏倚的方法，方法是将选择样本中的遗传变异与未选择下预期的遗传变异之间的相关性进行比较。我们检查了使用四个假设检验来识别所选样本中遗传变异之间的诱导关联。我们在蒙特卡罗模拟中评估了这些方法。最后,我们在一个应用例子中使用这些方法，使用来自英国生物银行(UKBB)的数据。拟议的测试表明，饮酒与选择UKBB之间存在关联。因此，以饮酒作为暴露或结果的UKBB分析可能会受到这种选择的偏见。
