The European Prospective Investigation into Cancer and Nutrition (EPIC) is an ongoing multi-centre prospective cohort study designed to investigate the relationship between nutrition and cancer, with the potential for studying other diseases as well. The study currently includes 519 978 participants (366 521 women and 153 457 men, mostly aged 35-70 years) in 23 centres located in 10 European countries, to be followed for cancer incidence and cause-specific mortality for several decades. At enrolment, which took place between 1992 and 2000 at each of the different centres, information was collected through a non-dietary questionnaire on lifestyle variables and through a dietary questionnaire addressing usual diet. Anthropometric measurements were performed and blood samples taken, from which plasma, serum, red cells and buffy coat fractions were separated and aliquoted for long-term storage, mostly in liquid nitrogen. To calibrate dietary measurements, a standardised, computer-assisted 24-hour dietary recall was implemented at each centre on stratified random samples of the participants, for a total of 36 900 subjects. EPIC represents the largest single resource available today world-wide for prospective investigations on the aetiology of cancers (and other diseases) that can integrate questionnaire data on lifestyle and diet, biomarkers of diet and of endogenous metabolism (e.g. hormones and growth factors) and genetic polymorphisms. First results of case-control studies nested within the cohort are expected early in 2003. The present paper provides a description of the EPIC study, with the aim of simplifying reference to it in future papers reporting substantive or methodological studies carried out in the EPIC cohort.

Cox's regression model is widely used for assessing associations between potential risk factors and disease occurrence in epidemiologic cohort studies. Although age is often a strong determinant of disease risk, authors have frequently used time-on-study instead of age as the time-scale, as for clinical trials. Unless the baseline hazard is an exponential function of age, this approach can yield different estimates of relative hazards than using age as the time-scale, even when age is adjusted for. We performed a simulation study in order to investigate the existence and magnitude of bias for different degrees of association between age and the covariate of interest. Age to disease onset was generated from exponential, Weibull or piecewise Weibull distributions, and both fixed and time-dependent dichotomous covariates were considered. We observed no bias upon using age as the time-scale. Upon using time-on-study, we verified the absence of bias for exponentially distributed age to disease onset. For non-exponential distributions, we found that bias could occur even when the covariate of interest was independent from age. It could be severe in case of substantial association with age, especially with time-dependent covariates. These findings were illustrated on data from a cohort of 84,329 French women followed prospectively for breast cancer occurrence. In view of our results, we strongly recommend not using time-on-study as the time-scale for analysing epidemiologic cohort data.

Considerable experimental and epidemiological evidence suggests that elevated endogenous sex steroids -notably androgens and oestrogens -promote breast tumour development. In spite of this evidence, postmenopausal androgen replacement therapy with dehydroepiandrosterone (DHEA) or testosterone has been advocated for the prevention of osteoporosis and improved sexual wellbeing. We have conducted a case-control study nested within the European Prospective Investigation into Cancer and Nutrition. Levels of DHEA sulphate (DHEAS), (D4-androstenedione), testosterone, oestrone, oestradiol and sex-hormone binding globulin (SHBG) were measured in prediagnostic serum samples of 677 postmenopausal women who subsequently developed breast cancer and 1309 matched control subjects. Levels of free testosterone and free oestradiol were calculated from absolute concentrations of testosterone, oestradiol and SHBG. Logistic regression models were used to estimate relative risks of breast cancer by quintiles of hormone concentrations. For all sex steroidsthe androgens as well as the oestrogens -elevated serum levels were positively associated with breast cancer risk, while SHBG levels were inversely related to risk. For the androgens, relative risk