IDEAS home Printed from https://ideas.repec.org/a/bla/jorssa/v164y2001i1p141-154.html
   My bibliography  Save this article

Overcoming biases and misconceptions in ecological studies

Author

Listed:
  • Katherine A. Guthrie
  • Lianne Sheppard

Abstract

The aggregate data study design provides an alternative group level analysis to ecological studies in the estimation of individual level health risks. An aggregate model is derived by aggregating a plausible individual level relative rate model within groups, such that population‐based disease rates are modelled as functions of individual level covariate data. We apply an aggregate data method to a series of fictitious examples from a review paper by Greenland and Robins which illustrated the problems that can arise when using the results of ecological studies to make inference about individual health risks. We use simulated data based on their examples to demonstrate that the aggregate data approach can address many of the sources of bias that are inherent in typical ecological analyses, even though the limited between‐region covariate variation in these examples reduces the efficiency of the aggregate study. The aggregate method has the potential to estimate exposure effects of interest in the presence of non‐linearity, confounding at individual and group levels, effect modification, classical measurement error in the exposure and non‐differential misclassification in the confounder.

Suggested Citation

  • Katherine A. Guthrie & Lianne Sheppard, 2001. "Overcoming biases and misconceptions in ecological studies," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(1), pages 141-154.
    • Handle: RePEc:bla:jorssa:v:164:y:2001:i:1:p:141-154
    DOI: 10.1111/1467-985X.00193
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/1467-985X.00193
    Download Restriction: no
    File URL: https://libkey.io/10.1111/1467-985X.00193?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. William H. Dow & Kristine A. Gonzalez & Luis Rosero-Bixby, 2003. "Aggregation and Insurance Mortality Estimation," NBER Working Papers 9827, National Bureau of Economic Research, Inc.
    2. Katherine A. Guthrie & Lianne Sheppard & Jon Wakefield, 2002. "A Hierarchical Aggregate Data Model with Spatially Correlated Disease Rates," Biometrics, The International Biometric Society, vol. 58(4), pages 898-905, December.
    3. Gillian A. Lancaster & Mick Green & Steven Lane, 2006. "Reducing bias in ecological studies: an evaluation of different methodologies," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(4), pages 681-700, October.
    4. Jesse J Plascak & James L Fisher, 2013. "Area-Based Socioeconomic Position and Adult Glioma: A Hierarchical Analysis of Surveillance Epidemiology and End Results Data," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-9, April.

    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:jorssa:v:164:y:2001:i:1:p:141-154. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.