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#### Article: Comparison of Three Approaches in the Analysis of Interaction between a Time-fixed Factor and a Time-varying Variable

Title Comparison of Three Approaches in the Analysis of Interaction between a Time-fixed Factor and a Time-varying Variable分析固定因素和伴时变量交互作用的三种方法比较 Ou, CChen, PWong, CM Poisson regressionCase-crossover approachCase-crossover methodInteraction 2010 中国卫生信息学会, 中国医科大学. The Journal's web site is located at http://zgwstj.periodicals.net.cn/ 中国卫生统计, 2010, v. 27 n. 2, p. 115-117 How to Cite?Chinese Journal of Health Statistics, 2010, v. 27 n. 2, p. 115-117 How to Cite? Objective This study aimed at illustrating three approaches for the analysis of interaction between a time-fixed factor and a time-varying variable,including Poisson regression,the case-only approach and case-crossover method.We also compared their statistical performance using a simulation study.Methods Two surrogate time-series of daily counts of mortality for smokers and non-smokers were produced using simulation.We analyzed the interaction between daily concentration of PM10 and smoking using three approaches.Their statistical performances were assessed by power and error.Results The case-only approach had the same performance with Poisson regression. Lower statistical power and bigger error were observed in the results from case-crossover method.Conclusion These three approaches are all useful tool in analysis of interaction between time-fixed factors and time-varying variables.Poisson regression and case-only approach provide better statistical performances. 目的本文將介紹分析固定因素和伴時變量交互作用的三種方法 :時間序列方法、單純病例研究法和病例交叉法,并通過模擬研究比較其優劣。方法計算機模擬吸煙和非吸煙人群的日死亡人數時間序列,用三種不同方法分析每日大氣可吸入顆粒物(PM10)濃度和吸煙對死亡影響的交互作用,比較各方法的檢驗效能和誤差。結果時間序列方法與單純病例研究法的檢驗效能和誤差相一致,而病例交叉法的檢驗效能略低,誤差略高于其他兩種方法。結論三種方法都是分析固定因素和伴時變量交互作用的有效工具。時間序列方法與單純病例研究法的表現略佳。 http://hdl.handle.net/10722/197269 1002-3674

DC FieldValueLanguage
dc.contributor.authorOu, Cen_US
dc.contributor.authorChen, Pen_US
dc.contributor.authorWong, CMen_US
dc.date.accessioned2014-05-23T02:32:03Z-
dc.date.available2014-05-23T02:32:03Z-
dc.date.issued2010en_US
dc.identifier.citation中国卫生统计, 2010, v. 27 n. 2, p. 115-117en_US
dc.identifier.citationChinese Journal of Health Statistics, 2010, v. 27 n. 2, p. 115-117-
dc.identifier.issn1002-3674-
dc.identifier.urihttp://hdl.handle.net/10722/197269-
dc.description.abstractObjective This study aimed at illustrating three approaches for the analysis of interaction between a time-fixed factor and a time-varying variable,including Poisson regression,the case-only approach and case-crossover method.We also compared their statistical performance using a simulation study.Methods Two surrogate time-series of daily counts of mortality for smokers and non-smokers were produced using simulation.We analyzed the interaction between daily concentration of PM10 and smoking using three approaches.Their statistical performances were assessed by power and error.Results The case-only approach had the same performance with Poisson regression. Lower statistical power and bigger error were observed in the results from case-crossover method.Conclusion These three approaches are all useful tool in analysis of interaction between time-fixed factors and time-varying variables.Poisson regression and case-only approach provide better statistical performances. 目的本文將介紹分析固定因素和伴時變量交互作用的三種方法 :時間序列方法、單純病例研究法和病例交叉法,并通過模擬研究比較其優劣。方法計算機模擬吸煙和非吸煙人群的日死亡人數時間序列,用三種不同方法分析每日大氣可吸入顆粒物(PM10)濃度和吸煙對死亡影響的交互作用,比較各方法的檢驗效能和誤差。結果時間序列方法與單純病例研究法的檢驗效能和誤差相一致,而病例交叉法的檢驗效能略低,誤差略高于其他兩種方法。結論三種方法都是分析固定因素和伴時變量交互作用的有效工具。時間序列方法與單純病例研究法的表現略佳。-
dc.languagechien_US
dc.publisher中国卫生信息学会, 中国医科大学. The Journal's web site is located at http://zgwstj.periodicals.net.cn/-
dc.relation.ispartof中国卫生统计en_US
dc.relation.ispartofChinese Journal of Health Statistics-
dc.subjectPoisson regression-
dc.subjectCase-crossover approach-
dc.subjectCase-crossover method-
dc.subjectInteraction-
dc.titleComparison of Three Approaches in the Analysis of Interaction between a Time-fixed Factor and a Time-varying Variableen_US
dc.title分析固定因素和伴时变量交互作用的三种方法比较-
dc.typeArticleen_US
dc.identifier.emailOu, C: cqou@HKUCC-COM.hku.hken_US
dc.identifier.emailChen, P: chenpy99@HKUCC.hku.hken_US
dc.identifier.emailWong, CM: hrmrwcm@hkucc.hku.hken_US
dc.identifier.authorityWong, CM=rp00338en_US
dc.identifier.doi10.3969/j.issn.1002-3674.2010.02.002-
dc.identifier.hkuros183768en_US
dc.identifier.volume27en_US
dc.identifier.issue2en_US
dc.identifier.spage115en_US
dc.identifier.epage117en_US
dc.publisher.placeChina-