Abstract
潘金仁,陈坤.队列研究资料相加交互作用可信区间的Bootstrap法估计[J].Chinese journal of Epidemiology,2010,31(7):808-811
队列研究资料相加交互作用可信区间的Bootstrap法估计
Bootstrap method-based estimation on the confidence interval for additive interaction in cohort studies
Received:December 17, 2009  
DOI:
KeyWord: 方法,Bootstrap  相加交互作用  队列研究
English Key Word: Bootstrap  Additive interaction  Cohort study
FundProject:国家科技重大专项(2009ZXl0004—901)
Author NameAffiliationE-mail
PAN Jin-ren Department of Epidemiology and Health Statistics, School of Mediciae, Zhejiang University, Hangzhou 310058, China  
CHEN Kun Department of Epidemiology and Health Statistics, School of Mediciae, Zhejiang University, Hangzhou 310058, China CK@zju.edu.cn 
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Abstract:
      交互作用评估是流行病学数据分析的重要环节,病因学研究中得到广泛应用的指数模型如logistic回归或Cox比例风险模型,常将危险因素的乘积项纳入模型,其乘积项系数反映了因素间的相乘交互作用,而在公共卫生方面交互作用分析应基于加法模型才更合适.文中根据Rothman提出的评估相加交互作用的指标,通过一个队列研究实例拟合Cox比例风险模型,应用RR值计算两因素的相加交互作用指标,并利用内置Bootstrap功能的S-Plus软件,较为方便地得到Bootstrap法估计的可信区间,避免队列研究资料应用OR值计算导致的估值偏差,且有更高的估计精度.相加和相乘交互作用分析的组合模式相当复杂,当两者冲突时宜选择加法模型.
English Abstract:
      Interaction assessment is an important step in epidemiological analysis. When etiological study is carried out, the logarithmic models such as logistic model or Cox proportional hazard model are commonly used to estimate the independent effects of the risk factors. However,estimating interaction between risk factors by the regression coefficient of the product term is on multiplicative scale, and for public-health purposes, it is supposed to be on additive scale or departure from additivity. This paper illustrates with a example of cohort study by fitting Cox proportional hazard model to estimate three measures for additive interaction which presented by Rothman.Adopting the S-Plus application with a built-in Bootstrap function, it is convenient to estimate the confidence interval for additive interaction. Furthermore, this method can avoid the exaggerated estimation by using ORs in a cohort study to gain better precision. When using the complex combination models between additive interaction and multiplicative interaction, it is reasonable to choose the former one when the result is inconsistent.
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