摘要
目的介绍不完全病例对照研究中基因与环境交互作用的估计方法。方法分别导出了logistic模型、对数线性模型在传统病例对照研究、单纯病例研究、不完全病例对照研究中主效应以及基因与环境交互作用效应的极大似然估计,并通过实例分析其应用价值。结果在传统病例对照研究中,当数据未缺失时,logistic模型与对数线性模型的结果是等价的。当无对照时,单纯病例研究的logistic模型可以估计基因与环境的交互作用。当对照组基因信息缺失但环境信息齐全时,用传统病例对照研究的logistic模型无法得到交互作用的估计;用单纯病例研究的logistic模型可以估计交互作用,但由于没有充分利用环境的信息,故得不到环境主效应的估计;不完全病例对照研究的对数线性模型,可同时得到交互作用和环境主效应的估计。结论不完全病例对照研究采用对数线性模型既可充分利用对照的环境暴露信息,估计环境的主效应,又可估计基因与环境的交互作用。当基因与环境暴露独立时,其估计值与完全数据是等价的。
Objective To introduce the approaches for estimating gene-environment interaction based on partial case-control studies. Methods The effects of logistic model and log-linear model for estimating the main effects and gene-environment interaction effect were estimated by means of maximum likelihood methods in traditional case-control studies, case-only studies and partial case-control studies, respectively. An example was also illustrated. Results In traditional case-control study with complete data,the results of logistic model and log-llnear model were equivalent. In case-only study without any information about controls, the logistic model can also efficiently estimate gene-environment interaction. In partial case-control study, environmental information was collected from all of the cases and controls, while genetic information was only collected from cases. For this case-control study with incomplete data, a suitable parameterized log-linear model could simultaneously and efficiently estimate the main effect of environment and gene-environment interaction, whereas the logistic model could not. Conclusion For a partial case-control study,log-linear model could estimate not only the main effect of environment but also gene-environment interaction. If genotype and exposure were independent, estimators from partial casecontrol were as precisely as those from complete-data case-control studies.
出处
《中华流行病学杂志》
CAS
CSCD
北大核心
2006年第1期72-75,共4页
Chinese Journal of Epidemiology
基金
国家重点基础研究发展计划(973)资助(2002CB512910)
江苏省高校自然科学基金重点项目资助(04KJB310081)
