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Epidemiology

  • Laurence Freedman
  • Mitchell H. Gail
  • Dale L. Preston
Chapter
  • 708 Downloads

Abstract

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.

Keywords

Quantile Groups Community Intervention Trial Radiation Epidemiology Informative Censoring Guest Researcher 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

Other publications by Ray Carroll cited in this chapter.

  1. Borkowf, C. B., Gail, M. H., Carroll, R. J., and Gill, R. D. (1997). Analyzing bivariate continuous data grouped into categories defined by empirical quantiles of marginal distributions. Biometrics, 53, 1054–1069.CrossRefzbMATHGoogle Scholar
  2. Carroll, R. J. (1989). Covariance analysis in generalized linear measurement error models. Statistics in Medicine, 8, 1075–1093.CrossRefGoogle Scholar
  3. 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
  4. Carroll, R. J., Gail, M. H., Benichou, J., and Pee, D. (2000). Score tests for familial correlation in genotyped-proband designs. Genetic Epidemiology, 18, 293–306.CrossRefGoogle Scholar
  5. Carroll, R. J., Gail, M. H., and Lubin, J. H. (1993). Case-control studies with errors in covariates. Journal of the American Statistical Association, 88, 185–199.zbMATHMathSciNetGoogle Scholar
  6. 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
  7. Carroll, R. J., Ruppert, D., and Stefanski, L. J. (1995). Measurement Error in Nonlinear Models. London: Chapman and Hall.CrossRefzbMATHGoogle Scholar
  8. Carroll, R. J., Ruppert, D., Stefanski, L. J., and Crainiceanu, C. M. (2006). Measurement Error in Nonlinear Models: a Modern Perspective, 2nd edition. Boca Raton: CRC Press.CrossRefGoogle Scholar
  9. 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
  10. 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
  11. Gail, M. H., Mark, S. D., Carroll, R. J., Green, S. B., and Pee, D. (1996). On design considerations and randomization-based inference for community intervention trials. Statistics in Medicine, 15, 1069–1092.CrossRefGoogle Scholar
  12. Gail, M. H., Pee, D., Benichou, J., and Carroll, R. (1999). Designing studies to estimate the penetrance of an identified autosomal dominant mutation: Cohort, case-control, and genotyped-proband designs. Genetic Epidemiology, 16, 15–39.CrossRefGoogle Scholar
  13. Gail M. H., Pfeiffer, R., Van Houwelingen, H. C., Carroll, R. J. (2000). On meta-analytic assessment of surrogate outcomes. Biostatistics, 1, 231–246.CrossRefzbMATHGoogle Scholar
  14. Kipnis, V., Carroll, R. J., Freedman, L. S., and Li, L. (1999). Implications of a new dietary measurement error model for estimation of relative risk: Application to four calibration studies. American Journal of Epidemiology, 150, 642–651.CrossRefGoogle Scholar
  15. Kuchenhoff, H. and Carroll, R. J. (1997). Segmented regression with errors in predictors: Semi-parametric and parametric methods. Statistics in Medicine, 16, 169–188.CrossRefGoogle Scholar
  16. 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
  17. Li, Y., Guolo, A., Hoffman, F. O., and Carroll, R. J. (2007). Shared uncertainty in measurement error problems, with application to Nevada Test Site fallout data. Biometrics, 63, 1226–1236.CrossRefzbMATHMathSciNetGoogle Scholar
  18. Lubin, J. H., Schafer, D. W., Ron, E., Stovall, M., and Carroll, R. J. (2004). A reanalysis of thyroid neoplasms in the Israeli tinea capitis study accounting for dose uncertainties. Radiation Research, 161, 359–368.CrossRefGoogle Scholar
  19. Schafer, D. W., Lubin, J. H., Ron, E., Stovall, M., and Carroll, R. J. (2001). Thyroid cancer following scalp irradiation: a reanalysis accounting for uncertainty in dosimetry. Biometrics, 57, 689–697.CrossRefzbMATHMathSciNetGoogle Scholar
  20. Spinka, C., Carroll, R. J., and Chatterjee, N. (2005). Analysis of case-control studies of genetic and environmental factors with missing genetic information and haplotype-phase ambiguity. Genetic Epidemiology, 29, 108–127.CrossRefGoogle Scholar
  21. Wu, M. C. and Carroll, R. J. (1988). Estimation and comparison of changes in the presence of informative right censoring by modeling the censoring process. Biometrics, 44, 175–188.CrossRefzbMATHMathSciNetGoogle Scholar
  22. 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.

  1. 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
  2. Prentice, R. L., Huang, Y., Tinker, L. F., Beresford, S. A., Lampe, J. W., and Neuhouser, M. L. (2009). Statistical aspects of the use of biomarkers in nutritional epidemiology research. Statistics in Biosciences, 1, 112–123.CrossRefGoogle Scholar
  3. Prentice, R. L. and Pyke, R. (1979). Logistic disease incidence models and case-control studies. Biometrika, 66, 403–411.CrossRefzbMATHMathSciNetGoogle Scholar
  4. Reeves, G. K., Cox, D. R., Darby, S. C., and Whitley, E. (1998). Some aspects of measurement error in explanatory variables for continuous and binary regression models. Statistics in Medicine, 17, 2157–2177.CrossRefGoogle Scholar
  5. 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

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Laurence Freedman
    • 1
  • Mitchell H. Gail
    • 2
  • Dale L. Preston
    • 3
  1. 1.Gertner Institute for EpidemiologyTel HashomerIsrael
  2. 2.National Cancer InstituteBethesdaUSA
  3. 3.Hirosoft InternationalEurekaUSA

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