Maximum Likelihood Inference On A Mixed Conditionally and Marginally Specified Regression Model for Genetic Epidemiologic Studies with Two-Phase Sampling
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两阶段抽样遗传流行病研究中条件与边际混合设定回归的最大似然推断
- 研究问题
- 在两阶段分层抽样中，如何同时估计遗传和环境变量对应的边际疾病风险，以及控制其他变量后的家族史条件风险？
- 方法与思路
- 作者构造兼容边际与条件风险设定的回归模型，分别在有无基因环境独立假设时发展最大似然方法，环境分布保持非参数。
- 材料陈述的贡献
- 摘要称建立替代似然方法及渐近理论，用罕见和常见遗传变异模拟比较方法与抽样策略，并展示结直肠腺瘤应用。
- 阅读关注点
- 应核对条件与边际设定的兼容性、两阶段抽样概率的处理，以及基因环境独立假设如何改变估计和效率。
- 阅读边界
- 仅依据所给 Crossref 摘要；未说明抽样可忽略性、识别条件或数值结果，疾病风险关联不能直接解释为因果效应。
原文摘要
Summary Two-phase stratified sampling designs can reduce the cost of genetic epidemiologic studies by limiting expensive ascertainments of genetic and environmental exposure to an efficiently selected subsample (phase II) of the main study (phase I). Family history and some covariate information, which may be cheaply gathered for all subjects at phase I, can be used for sampling of informative subjects at phase II. We develop alternative maximum likelihood methods for analysis of data from such studies by using a novel regression model that permits the estimation of ‘marginal’ risk parameters that are associated with the genetic and environmental covariates of interest, while simultaneously characterizing the ‘conditional’ risk of the disease associated with family history after adjusting for the other covariates. The methods and appropriate asymptotic theories are developed with and without an assumption of gene–environment independence, allowing the distribution of the environmental factors to remain non-parametric. The performance of the alternative methods and of sampling strategies is studied by using simulated data involving rare and common genetic variants. An application of the methods proposed is illustrated by using a case–control study of colorectal adenoma embedded within the prostate, lung, colorectal and ovarian cancer screening trial.