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Overcoming biases and misconceptions in ecological studies

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  • 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
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.

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