%0 Journal Article
%T Risk prediction models with incomplete data with application to prediction of estrogen receptor-positive breast cancer: prospective data from the Nurses' Health Study
%A Bernard Rosner
%A Graham A Colditz
%A J Dirk Iglehart
%A Susan E Hankinson
%J Breast Cancer Research
%D 2008
%I BioMed Central
%R 10.1186/bcr2110
%X 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.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 ¡À 0.007 to 0.645 ¡À 0.007 (P < 0.001) after the addition of imputed estradiol.Circulating estradiol levels in postmenopausal women appear to add to other lifestyle factors in predicting a woman's individual risk of breast cancer.Breast cancer risk prediction models have been developed for use as an entry criterion into breast cancer chemoprevention trials (for example, National Surgical Adjuvant Breast and Bowel Project tamoxifen trial and the Study of Tamoxifen and Raloxifene), in counseling women on the potential use of chemopreventives, and to provide insight into a woman's individual breast cancer risk [1-4]. The initial Gail model incorporated a subset of breast cancer risk factors, namely age, age at menarche, age at first birth, family history of breast cancer or of atypical hyperplasia, and history of breast biopsies [5,6]. Subsequently, several groups have developed more extensive statistical models that incorporate a greater number of breast cancer risk factors [1,4].In postmenopausal women, circulating levels of estradiol predict subse
%U http://breast-cancer-research.com/content/10/4/R55