Further development of the case-only design for assessing gene-environment interaction: evaluation of and adjustment for bias
Further development of the case-only design for assessing gene-environment interaction: evaluation of and adjustment for bias
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DOI：
10.1093/ije/dyh306
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发表时间：
2004-10-01
影响因子：
7.7
通讯作者：
Ahsan, H
中科院分区：
文献类型：
--
作者：
Gatto, NM;Campbell, UB;Ahsan, H
研究基因-环境相互作用的病例研究比病例对照分析提供了更高的统计效率。这种设计因易受人群中遗传和环境因素之间不独立而产生的偏差而受到批评。考虑到独立性对仅病例相互作用估计的有效性至关重要，研究人员经常使用对照来评估独立性假设是否成立，正如文献中建议的那样。我们的工作调查了这种方法在多大程度上是合适的，以及如何在个案分析中解释非独立性。方法用流行病学术语给出一个公式，说明在对照中测量的基因-环境关联与源人群中测量的基因-环境关联之间的关系。使用该公式，我们进行了敏感性分析，以描述在评估基因-环境独立性时，控制可以用作源种群代理的情况。最后，我们生成了假设的队列数据，以检验多变量建模方法是否可以用于控制非独立性。结果我们的敏感性分析表明，即使基线疾病风险较低（即1%），相互作用和独立效应适中（即风险比= 2），也不应使用对照来评估人群中的基因-环境独立性。当因素相关联时，在个案分析中使用标准的统计多变量技术可以消除由非独立性引起的偏差。结论：即使在疾病风险较低的情况下，对对照组的基因-环境独立性的评估也不能为病例研究中的偏倚提供一致的检验。考虑到当非独立性的来源可以概念化时，对非独立性的控制是可能的，仅病例设计可能仍然是检查基因-环境相互作用的有用流行病学工具。
Background The case-only study for investigating gene-environment interactions provides increased statistical efficiency over case-control analyses. This design has been criticized for being susceptible to bias arising from non-independence between the genetic and environmental factors in the population. Given that independence is critical to the validity of case-only estimates of interaction, researchers frequently use controls to evaluate whether the independence assumption is tenable, as advised in the literature. Our work investigates to what extent this approach is appropriate and how non-independence can be accounted for in case-only analyses.Methods We provide a formula in epidemiological terms that illustrates the relationship between the gene-environment association measured among controls and the gene-environment association in the source population. Using this formula, we conducted sensitivity analyses to describe the circumstances in which controls can be used as proxy for the source population when evaluating gene-environment independence. Lastly, we generated hypothetical cohort data to examine whether multivariable modelling approaches can be used to control for non-independence.Results Our sensitivity analyses show that controls should not be used to evaluate gene-environment independence in the population, even when the baseline risk of disease is low (i.e. 1%), and the interaction and independent effects are moderate (i.e. risk ratio = 2). When the factors are associated, it is possible to remove bias arising from non-independence using standard statistical multivariable techniques in case-only analyses.Conclusions Even when the disease risk is low, evaluation of gene-environment independence in controls does not provide a consistent test for bias in the case-only study. Given that control for non-independence is possible when the source of the non-independence can be conceptualized, the case-only design may still be a useful epidemiological tool for examining gene-environment interactions.