Functional Methods for Radiation Exposure and Biomarker Data

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DESCRIPTION (provided by applicant): The Radiation Effects Research Foundation (RERF) provides very rich data on the atomic bomb survivors. Although some measurement error methods have been applied to adjust for radiation measurement error for RERF data, these existing methods generally rely on certain parametric measurement error assumptions. Therefore, it is important to further develop semiparametric or nonparametric methods that do not need these parametric assumptions that are difficult to test. Dosimetry data may be considered as a surrogate variable for the unobserved underlying radiation exposure. A biomarker such as percentage of cells with stable chromosome aberrations can be treated as a type of instrumental variable for the un- observed radiation dose. From a defined cohort of about 120,000 A-bomb survivors (the Life Span Study, LSS, cohort), the subcohort of about 4,000 who have DS02 radiation dose estimates, stable chromosome aberration data, and outcome data for diseases such as cardiovascular disease, stomach cancer, lung cancer, or breast cancer, will comprise the calibration sample. By using data from the calibration sample, we can estimate radiation dose responses for the entire LSS, with an appropriate adjustment for the uncertainty in DS02 dose estimates. An important result here is that the measurement error standard deviation will not have an assumed value, but rather will be estimated from the data, even though the data do not include replicate measurements or estimates of radiation doses. In the proposal, the regression problem of interest will have a main cohort that has radiation estimation for all subjects, but multiple radiation-related variables available in a subcohort. Specific foci of this proposal include: (i) Methods for logistic regression dose-response models, when the exposure estimates are subject to classical additive errors and biomarker data are available for some subjects. (ii) Methods for survival dose-response models, when the exposure estimates are subject to classical additive errors and biomarker data are available for some subjects. (iii) Methods for logistic and Cox regression dose-response models, when the expo- sure estimates are subject to mixtures of Berkson and classical errors and biomarker data are available for some subjects. The new methods will also be applied to the doubly labeled water data from the Nutritional Biomarker Study of the Womens Health Initiative. The proposed methods will have general applications to any analysis of exposure-disease relationships in which exposures are measured with error and potential instrumental variables are available for a calibration sample. PUBLIC HEALTH RELEVANCE: We propose to use semiparametric or nonparametric methods to adjust for the effects of radiation dose measurement error on the estimation of radiation dose responses for health effects in survivors of the atomic bombings of Hiroshima and Nagasaki. Our proposed approaches will treat stable chromosome aberration data as an instrumental variable. The proposed methods will also be applied to the doubly labeled water data from the Nutritional Biomarker Study of the Womens Health Initiative, and will have general applications to any analysis of exposure-disease relationships in which exposures are measured with error and potential instrumental variables are available for a subsample.