IDEAS home Printed from https://ideas.repec.org/a/bla/jorssb/v70y2008i1p73-93.html
   My bibliography  Save this article

The combination of ecological and case–control data

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-9868.2007.00628.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1467-9868.2007.00628.x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sebastien J-P. A. Haneuse & Jonathan C. Wakefield, 2007. "Hierarchical Models for Combining Ecological and Case–Control Data," Biometrics, The International Biometric Society, vol. 63(1), pages 128-136, March.
    2. Katie Wilson & Jon Wakefield, 2022. "A probabilistic model for analyzing summary birth history data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 47(11), pages 291-344.
    3. Irene L. Hudson & Linda Moore & Eric J. Beh & David G. Steel, 2010. "Ecological inference techniques: an empirical evaluation using data describing gender and voter turnout at New Zealand elections, 1893–1919," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(1), pages 185-213, January.
    4. Esmerelda A. Ramalho & Richard Smith, 2003. "Discrete choice non-response," CeMMAP working papers 07/03, Institute for Fiscal Studies.
    5. Shuai Shao & Göran Kauermann, 2020. "Understanding price elasticity for airline ancillary services," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 19(1), pages 74-82, February.
    6. Puig, Xavier & Ginebra, Josep, 2014. "A cluster analysis of vote transitions," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 328-344.
    7. Rob Eisinga, 2009. "The beta‐binomial convolution model for 2×2 tables with missing cell counts," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 63(1), pages 24-42, February.
    8. Brady Ryan & Ananthika Nirmalkanna & Candemir Cigsar & Yildiz E. Yilmaz, 2023. "Evaluation of Designs and Estimation Methods Under Response-Dependent Two-Phase Sampling for Genetic Association Studies," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(2), pages 510-539, July.
    9. Antonio Forcina & Davide Pellegrino, 2019. "Estimation of voter transitions and the ecological fallacy," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(4), pages 1859-1874, July.
    10. Xiaofei Wang & Haibo Zhou, 2006. "A Semiparametric Empirical Likelihood Method for Biased Sampling Schemes with Auxiliary Covariates," Biometrics, The International Biometric Society, vol. 62(4), pages 1149-1160, December.
    11. Esmeralda A. Ramalho & Richard J. Smith, 2013. "Discrete Choice Non-Response," Review of Economic Studies, Oxford University Press, vol. 80(1), pages 343-364.
    12. Constantine E. Frangakis & Stuart G. Baker, 2001. "Compliance Subsampling Designs for Comparative Research: Estimation and Optimal Planning," Biometrics, The International Biometric Society, vol. 57(3), pages 899-908, September.
    13. Carolina Plescia & Lorenzo De Sio, 2018. "An evaluation of the performance and suitability of R × C methods for ecological inference with known true values," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(2), pages 669-683, March.
    14. Nathan Kallus & Xiaojie Mao & Angela Zhou, 2022. "Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination," Management Science, INFORMS, vol. 68(3), pages 1959-1981, March.
    15. D. James Greiner & Kevin M. Quinn, 2009. "R×C ecological inference: bounds, correlations, flexibility and transparency of assumptions," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 67-81, January.
    16. Yuichi Hirose, 2011. "Efficiency of profile likelihood in semi-parametric models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 63(6), pages 1247-1275, December.
    17. Arie ten Cate, 2014. "Maximum likelihood estimation of the Markov chain model with macro data and the ecological inference model," CPB Discussion Paper 284.rdf, CPB Netherlands Bureau for Economic Policy Analysis.
    18. Wenguang Sun & Marshall M. Joffe & Jinbo Chen & Steven M. Brunelli, 2010. "Design and Analysis of Multiple Events Case–Control Studies," Biometrics, The International Biometric Society, vol. 66(4), pages 1220-1229, December.
    19. 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.
    20. Prosenjit Kundu & Nilanjan Chatterjee, 2023. "Logistic regression analysis of two‐phase studies using generalized method of moments," Biometrics, The International Biometric Society, vol. 79(1), pages 241-252, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jorssb:v:70:y:2008:i:1:p:73-93. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.