HIERARCHICAL REGRESSION-ANALYSIS APPLIED TO A STUDY OF MULTIPLE DIETARY EXPOSURES AND BREAST-CANCER

被引：95

作者：

WITTE, JS

GREENLAND, S

HAILE, RW

BIRD, CL

机构：

[1] Department of Epidemiology, Los Angeles, CA

[2] Department of Community Health Sciences, UCLA School of Public Health, Los Angeles, CA

来源：

EPIDEMIOLOGY | 1994年 / 5卷 / 06期

关键词：

BAYESIAN STATISTICS; EPIDEMIOLOGIC METHODS; BREAST NEOPLASMS; NUTRITION; DIET;

D O I：

10.1097/00001648-199411000-00009

中图分类号：

R1 [预防医学、卫生学];

学科分类号：

1004 ; 120402 ;

摘要：

Hierarchical regression attempts to improve standard regression estimates by adding a second-stage ''prior'' regression to an ordinary model. Here, we use hierarchical regression to analyze case-control data on diet and breast cancer. This regression yields semi-Bayes relative risk estimates for dietary items by using a second-stage model to pull estimates reward each other when the corresponding variables have similar levels of nutrients. Unlike classical Bayesian analysis, however, no use is made of previous studies on nutrient effects. Compared with results obtained with one-stage conditional maximum-likelihood logistic regression, our hierarchical regression model gives more stable and plausible estimates. In particular, certain effects with implausible maximum likelihood estimates have more reasonable semi-Bayes estimates.

引用

页码：612 / 621

页数：10

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