Sensitivity analysis on the ecological bias for Seoul tuberculosis data

Sensitivity analysis on the ecological bias for Seoul tuberculosis data In ecological studies, researchers often try to convey the analysis results to individual level based on aggregate data. In order to do this correctly, the possibility of ecological bias should be studied and addressed. One of the key ideas used to address the ecological bias issue is to derive the ecological model from the individual model and to check whether the parameter of interest in the individual model is identifiable in the ecological model. However, the procedure depends on unverifiable assumptions, and we recommend checking how sensitive the results are to these unverifiable assumptions. We analyzed the tuberculosis data that was collected in Seoul in 2005 using a spatial ecological regression model for the aggregate count data with spatial correlation, and found that the deprivation index is likely to have a small positive effect on the occurrence risk of tuberculosis in individual level in Seoul. We considered this finding in various aspects by performing in depth sensitivity analyses. In particular, our findings are shown to be robust to the distribution assumptions for the individual exposure and missing binary covariate across various scenarios. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Environmental and Ecological Statistics Springer Journals

Sensitivity analysis on the ecological bias for Seoul tuberculosis data

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Publisher
Springer US
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Life Sciences; Ecology; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Math. Appl. in Environmental Science; Statistics for Life Sciences, Medicine, Health Sciences; Theoretical Ecology/Statistics
ISSN
1352-8505
eISSN
1573-3009
D.O.I.
10.1007/s10651-018-0408-4
Publisher site
See Article on Publisher Site

Abstract

In ecological studies, researchers often try to convey the analysis results to individual level based on aggregate data. In order to do this correctly, the possibility of ecological bias should be studied and addressed. One of the key ideas used to address the ecological bias issue is to derive the ecological model from the individual model and to check whether the parameter of interest in the individual model is identifiable in the ecological model. However, the procedure depends on unverifiable assumptions, and we recommend checking how sensitive the results are to these unverifiable assumptions. We analyzed the tuberculosis data that was collected in Seoul in 2005 using a spatial ecological regression model for the aggregate count data with spatial correlation, and found that the deprivation index is likely to have a small positive effect on the occurrence risk of tuberculosis in individual level in Seoul. We considered this finding in various aspects by performing in depth sensitivity analyses. In particular, our findings are shown to be robust to the distribution assumptions for the individual exposure and missing binary covariate across various scenarios.

Journal

Environmental and Ecological StatisticsSpringer Journals

Published: May 29, 2018

References

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