Raymond Carroll’s work has had an important impact on epidemiologic research. This article reviews contributions to theory for the case–control design and to methods for nutritional and radiation epidemiology. Some of these contributions build on Ray’s broad-ranging research on regression analysis, measurement error, and missing data problems. Ray has been a welcome visitor at the U. S. National Institutes of Health (NIH), first with the National Heart, Lung, and Blood Institute and later with the National Cancer Institute (NCI), both as a Visiting Scientist and Guest Researcher and as a friendly collaborator who drops by from time to time. At NIH, he has given valuable advice on a wide range of topics and collaborated on many projects not covered by this article, including the analysis of survival data with informative censoring (Wu and Carroll, 1988 [OW-2]), the design of community intervention trials (Gail et al., 1996), the design and analysis of the “kin-cohort” design for genetic epidemiology (Carroll et al., 2000 Gail et al., 1999), the meta-analysis of surrogate endpoints (Gail, 2000), and agreement of exposure assessments based on quantile groupings (Borkowf et al., 1997), among many others.
KeywordsQuantile Groups Community Intervention Trial Radiation Epidemiology Informative Censoring Guest Researcher
Other publications by Ray Carroll cited in this chapter.
- Carroll, R. J. (1999). Risk assessment with subjectively derived doses. In Uncertainties in Radiation Dosimetry and Their Impact on Dose-Response Analysis, E. Ron and F. O. Hoffman (eds), 37–51. Bethesda, MD: National Cancer Institute Press.Google Scholar
- Carroll, R. J., Midthune, D., Subar, A. F., Shumakovich, M., Freedman, L. S., Thompson, F. E., and Kipnis, V. (2012). Taking advantage of the strengths of two different dietary assessment instruments to improve intake estimates for nutritional epidemiology. American Journal of Epidemiology, 175, 340–347.CrossRefGoogle Scholar
- Carroll, R. J., Schafer, D. W., Lubin, J. H., Ron, E., and Stovall, M. (2000b). Thyroid cancer after scalp irradiation: a reanalysis accounting for uncertainty in dosimetry. Radiation Research, 154, 721–722; discussion 723–724.Google Scholar
- Freedman, L. S., Midthune, D., Carroll, R. J., Tasevska, N., Schatzkin, A., Mares, J., Tinker, L., Potischman, N., and Kipnis, V. (2011). Using regression calibration equations that combine self-reported intake and biomarker measures to obtain unbiased estimates and more powerful tests of dietary associations. American Journal of Epidemiology, 174, 1238–1245.CrossRefGoogle Scholar
- Kukush, A., Shklyar, S., Masiuk, S., Likhtarov, I., Kovgan, L., Carroll, R. J., and Bouville, A. (2011). Methods for estimation of radiation risk in epidemiological studies accounting for classical and berkson errors in doses. International Journal of Biostatistics, 7, Article 15.Google Scholar
- Zhang, S. J., Midthune, D., Guenther, P. M., Krebs-Smith, S. M., Kipnis, V., Dodd, K. W., Buckman, D. W., Tooze, J. A., Freedman, L. S., and Carroll, R. J. (2011). A new multivariate measurement error model with zero-inflated dietary data, and its application to dietary assessment. Annals of Applied Statistics, 5, 1456–1487.CrossRefzbMATHMathSciNetGoogle Scholar
Publications by other authors cited in this chapter.
- National Research Council (1984). NAS/NRC Committee on Radioepidemiological Tables. Assigned Share for Radiation as a Cause of Cancer – Review of Radioepidemiological Tables. Assigning Probabilities of Causation (Final Report). Washington, DC: National Academies Press.Google Scholar
- Ron, E. and Hoffman, F. O. (eds) (1999). Uncertainties in Radiation Dosimetry and Their Impact on Dose-Response Analysis. Bethesda, MD: National Cancer Institute.Google Scholar