Liang Liang, Ma Yanyuan, Carroll Raymond J
Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA.
Department of Statistics, Penn State University, University Park, PA 16802, USA.
J Multivar Anal. 2019 Sep;173:38-50. doi: 10.1016/j.jmva.2019.01.006. Epub 2019 Feb 8.
Case-controls studies are popular epidemiological designs for detecting gene-environment interactions in the etiology of complex diseases, where the genetic susceptibility and environmental exposures may often be reasonably assumed independent in the source population. Various papers have presented analytical methods exploiting gene-environment independence to achieve better efficiency, all of which require either a rare disease assumption or a distributional assumption on the genetic variables. We relax both assumptions. We construct a semiparametric estimator in case-control studies exploiting gene-environment independence, while the distributions of genetic susceptibility and environmental exposures are both unspecified and the disease rate is assumed unknown and is not required to be close to zero. The resulting estimator is semiparametric efficient and its superiority over prospective logistic regression, the usual analysis in case-control studies, is demonstrated in various numerical illustrations.
病例对照研究是检测复杂疾病病因中基因与环境相互作用的常用流行病学设计，在源人群中，通常可以合理地假设遗传易感性和环境暴露是独立的。各种论文都提出了利用基因与环境独立性的分析方法以提高效率，所有这些方法都需要罕见病假设或对遗传变量的分布假设。我们放宽了这两个假设。我们在病例对照研究中构建了一个利用基因与环境独立性的半参数估计量，而遗传易感性和环境暴露的分布均未明确指定，并且疾病发生率被假定为未知，且不需要接近零。所得估计量是半参数有效的，并且在各种数值示例中证明了其相对于前瞻性逻辑回归（病例对照研究中的常用分析方法）的优越性。
