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The combination of ecological and case–control data

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  • Sebastien J.‐P. A. Haneuse
  • And Jonathan C. Wakefield

Abstract

Summary. Ecological studies, in which data are available at the level of the group, rather than at the level of the individual, are susceptible to a range of biases due to their inability to characterize within‐group variability in exposures and confounders. To overcome these biases, we propose a hybrid design in which ecological data are supplemented with a sample of individual level case–control data. We develop the likelihood for this design and illustrate its benefits via simulation, both in bias reduction when compared with an ecological study and in efficiency gains relative to a conventional case–control study. An interesting special case of the design proposed is the situation where ecological data are supplemented with case‐only data. The design is illustrated by using a data set of county‐specific lung cancer mortality rates in the state of Ohio from 1988.

Suggested Citation

  • Sebastien J.‐P. A. Haneuse & And Jonathan C. Wakefield, 2008. "The combination of ecological and case–control data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 73-93, February.
  • Handle: RePEc:bla:jorssb:v:70:y:2008:i:1:p:73-93
    DOI: 10.1111/j.1467-9868.2007.00628.x
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    1. N. E. Breslow & N. Chatterjee, 1999. "Design and analysis of two‐phase studies with binary outcome applied to Wilms tumour prognosis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 48(4), pages 457-468.
    2. Jon Wakefield, 2003. "Sensitivity Analyses for Ecological Regression," Biometrics, The International Biometric Society, vol. 59(1), pages 9-17, March.
    3. Jon Wakefield, 2004. "Ecological inference for 2 × 2 tables (with discussion)," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(3), pages 385-445, July.
    4. Judge, Ken & Mulligan, Jo-Ann & Benzeval, Michaela, 0. "Income inequality and population health," Social Science & Medicine, Elsevier, vol. 46(4-5), pages 567-579, February.
    5. J. F. Lawless & J. D. Kalbfleisch & C. J. Wild, 1999. "Semiparametric methods for response‐selective and missing data problems in regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(2), pages 413-438, April.
    6. Manski, Charles F & Lerman, Steven R, 1977. "The Estimation of Choice Probabilities from Choice Based Samples," Econometrica, Econometric Society, vol. 45(8), pages 1977-1988, November.
    7. Jon Wakefield, 2004. "Ecological inference for 2 × 2 tables," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(3), pages 385-425, July.
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