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R.L. Prentice 363from urinary excretion markers. When those biomarker measures are comparedto self-report data, one sees strong positive measurement error correlationsamong FFQs, 24-HRs and four-day food records (4-DFRs); see Prenticeet al. (2011).The implication is that biomarker data, but not data using another selfreport,need to be used to assess self-report measurement error, and to calibratethe self-report data for use in nutritional epidemiology association studies.Studies to date using this type of regression calibration approach tendto give quite different results from traditional analyses based on self-reportdata alone, for example, with strong positive associations between total energyconsumption with heart disease and breast, colorectal and total cancerincidence; see, e.g., Prentice and Huang (2011).From a statistical modeling perspective, calibrated dietary exposure estimatestypically arise from linear regression of (log-transformed) biomarkervalues on corresponding self-report estimates and on such study subject characteristicsas body mass index, age, and ethnicity. These latter variables arequite influential in explaining biomarker variation, as may in part reflect systematicbiases in dietary reporting. For example, while persons of normalweight tend to show little energy under-reporting, obese persons underestimatesubstantially, in the 30–50% range on average (Heitmann and Lissner,1995). These types of systematic biases can play havoc with disease associationanalyses if not properly addressed.Measurement error correction methods are not easy for nutritional epidemiologiststo grasp, and are not so easy even for nutritionally-orientedstatisticians. A logical extension of the biomarker calibration work conductedto date is a major research emphasis on nutritional biomarker development, toproduce measurement error–corrected consumption estimates for many morenutrients and foods. Statisticians, in conjunction with nutritional and epidemiologicalcolleagues, can play a major role in establishing the rationalefor, and the design of, such a nutritional biomarker development enterprise,which may entail the conduct of sizeable human feeding studies. For example,such a feeding study among 150 free-living Seattle participants in theWomen’s Health Initiative is currently nearing completion, and will examinecandidate biomarkers and higher dimensional metabolic profiles for novelnutritional biomarker development.32.4 Preventive intervention development and testingClosely related to the development of biomarkers of exposure, is the use ofbiomarkers for preventive intervention development. While there is a ratherlarge enterprise for the development of therapeutic interventions, the developmentof innovative disease prevention interventions is less impressive. One