Avoiding bias from aggregate measures of exposure

被引：6

作者：

Duffy, Stephen W.

Jonsson, Hakan

Agbaje, Olorunsola F.

Pashayan, Nora

Gabe, Rhian

机构：

[1] Cancer Research UK Centre for Epidemiology, Mathematics and Statistics, Wolfson Institute of Preventive Medicine, London EC1M 6BQ, Charterhouse Square

[2] Cancer Research UK Centre for Epidemiology, Mathematics and Statistics, Wolfson Institute of Preventive Medicine, London

[3] Department of Radiation Sciences, Oncology, Umeå University, Umeå

[4] Department of Public Health and Primary Care, Institute of Public Health, University Forvie Site, Cambridge

来源：

JOURNAL OF EPIDEMIOLOGY AND COMMUNITY HEALTH | 2007年 / 61卷 / 05期

关键词：

D O I：

10.1136/jech.2006.050203

中图分类号：

R1 [预防医学、卫生学];

学科分类号：

1004 ; 120402 ;

摘要：

Background: Sometimes in descriptive epidemiology or in the evaluation of a health intervention policy change, proportions exposed to a risk factor or to an intervention are used as explanatory variables in log-linear regressions for disease incidence or mortality. Aim: To demonstrate how estimates from such models can be substantially inaccurate as estimates of the effect of the risk factor or intervention at individual level. To show how the individual level effect can be correctly estimated by excess relative risk models. Methods: The problem and solution are demonstrated using data on prostate-specific antigen testing and prostate cancer incidence.

引用

页码：461 / 463

页数：3

共 9 条

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