Format

Send to:

Choose Destination
See comment in PubMed Commons below
Breast Cancer Res. 2008;10(4):R55. doi: 10.1186/bcr2110. Epub 2008 Jul 3.

Risk prediction models with incomplete data with application to prediction of estrogen receptor-positive breast cancer: prospective data from the Nurses' Health Study.

Author information

  • 1Channing Laboratory, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 181 Longwood Ave, Boston, MA 02115, USA. stbar@channing.harvard.edu

Abstract

INTRODUCTION:

A number of breast cancer risk prediction models have been developed to provide insight into a woman's individual breast cancer risk. Although circulating levels of estradiol in postmenopausal women predict subsequent breast cancer risk, whether the addition of estradiol levels adds significantly to a model's predictive power has not previously been evaluated.

METHODS:

Using linear regression, the authors developed an imputed estradiol score using measured estradiol levels (the outcome) and both case status and risk factor data (for example, body mass index) from a nested case-control study conducted within a large prospective cohort study and used multiple imputation methods to develop an overall risk model including both risk factor data from the main cohort and estradiol levels from the nested case-control study.

RESULTS:

The authors evaluated the addition of imputed estradiol level to the previously published Rosner and Colditz log-incidence model for breast cancer risk prediction within the larger Nurses' Health Study cohort. The follow-up was from 1980 to 2000; during this time, 1,559 invasive estrogen receptor-positive breast cancer cases were confirmed. The addition of imputed estradiol levels significantly improved risk prediction; the age-specific concordance statistic increased from 0.635 +/- 0.007 to 0.645 +/- 0.007 (P < 0.001) after the addition of imputed estradiol.

CONCLUSION:

Circulating estradiol levels in postmenopausal women appear to add to other lifestyle factors in predicting a woman's individual risk of breast cancer.

PMID:
18598349
[PubMed - indexed for MEDLINE]
PMCID:
PMC2575548
Free PMC Article
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for BioMed Central Icon for PubMed Central
    Loading ...
    Write to the Help Desk