估计分层两阶段病例对照设计中的加性相互作用效应

IF 1.1 4区 生物学 Q4 GENETICS & HEREDITY
Human Heredity Pub Date : 2019-01-01 Epub Date: 2019-10-21 DOI:10.1159/000502738
Ai Ni, Jaya M Satagopan
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引用次数: 0

摘要

背景和目的:在病例对照研究中,流行病学对估计两种危险因素之间的加性相互作用效应有相当大的兴趣。累加性相互作用定义为与一个因素相关的绝对风险在另一个因素的不同水平之间的差异降低。分层两阶段病例对照设计在流行病学中常用,以减少协变量的组装成本。通过考虑潜在的分层方案来获得模型参数的有效估计,以获得准确和精确的加性相互作用效应估计是至关重要的。本文的目的是研究在分层两阶段病例对照设计下估计模型参数和加性相互作用效应的不同方法的性质。方法:通过模拟研究了三种现有方法的性质,即地层特定偏移、逆概率加权和多重插值,用于估计模型参数和加性相互作用效应。我们还使用两项已发表的流行病学研究的数据来说明这些特性。结果:仿真研究表明,当真实模型和分析模型都是可加性的(即,不包括乘法交互项)时,多重输入方法表现良好,但当分析模型是非可加性的(即,包括乘法交互项)时,与偏移方法相比,多重输入方法没有明显的优势。当分析模型包含乘法相互作用效应时,偏移法显示出最佳的综合性能。结论:在分层两期病例对照研究中,在估计危险因素之间的加性相互作用时,我们建议在分析模型为加性时使用多重imputation来估计模型参数,而在分析模型为非加性时,我们建议使用偏移法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimating Additive Interaction Effect in Stratified Two-Phase Case-Control Design.

Background and aims: There is considerable interest in epidemiology to estimate an additive interaction effect between two risk factors in case-control studies. An additive interaction is defined as the differential reduction in absolute risk associated with one factor between different levels of the other factor. A stratified two-phase case-control design is commonly used in epidemiology to reduce the cost of assembling covariates. It is crucial to obtain valid estimates of the model parameters by accounting for the underlying stratification scheme to obtain accurate and precise estimates of additive interaction effects. The aim of this paper is to examine the properties of different methods for estimating model parameters and additive interaction effects under a stratified two-phase case-control design.

Methods: Using simulations, we investigate the properties of three existing methods, namely stratum-specific offset, inverse-probability weighting, and multiple imputation for estimating model parameters and additive interaction effects. We also illustrate these properties using data from two published epidemiology studies.

Results: Simulation studies show that the multiple imputation method performs well when both the true and analysis models are additive (i.e., does not include multiplicative interaction terms) but does not provide a discernible advantage over the offset method when the analysis models are non-additive (i.e., includes multiplicative interaction terms). The offset method exhibits the best overall properties when the analysis model contains multiplicative interaction effects.

Conclusion: When estimating additive interaction between risk factors in stratified two-phase case-control studies, we recommend estimating model parameters using multiple imputation when the analysis model is additive, and we recommend the offset method when the analysis model is non-additive.

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来源期刊
Human Heredity
Human Heredity 生物-遗传学
CiteScore
2.50
自引率
0.00%
发文量
12
审稿时长
>12 weeks
期刊介绍: Gathering original research reports and short communications from all over the world, ''Human Heredity'' is devoted to methodological and applied research on the genetics of human populations, association and linkage analysis, genetic mechanisms of disease, and new methods for statistical genetics, for example, analysis of rare variants and results from next generation sequencing. The value of this information to many branches of medicine is shown by the number of citations the journal receives in fields ranging from immunology and hematology to epidemiology and public health planning, and the fact that at least 50% of all ''Human Heredity'' papers are still cited more than 8 years after publication (according to ISI Journal Citation Reports). Special issues on methodological topics (such as ‘Consanguinity and Genomics’ in 2014; ‘Analyzing Rare Variants in Complex Diseases’ in 2012) or reviews of advances in particular fields (‘Genetic Diversity in European Populations: Evolutionary Evidence and Medical Implications’ in 2014; ‘Genes and the Environment in Obesity’ in 2013) are published every year. Renowned experts in the field are invited to contribute to these special issues.
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