Overcoming Biases and Misconceptions in Ecological Studies
Summary 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 disea...
| Published in: | Journal of the Royal Statistical Society Series A: Statistics in Society |
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| Main Authors: | , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Oxford University Press (OUP)
2001
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| Subjects: | |
| Online Access: | http://dx.doi.org/10.1111/1467-985x.00193 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2F1467-985X.00193 https://onlinelibrary.wiley.com/doi/pdf/10.1111/1467-985X.00193 https://academic.oup.com/jrsssa/article-pdf/164/1/141/49761162/jrsssa_164_1_141.pdf |
| Summary: | Summary 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. |
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