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Reducing bias in ecological studies: an evaluation of different methodolgies.

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Reducing bias in ecological studies: an evaluation of different methodolgies. / Lancaster, Gillian; Green, Mick; Lane, Steven.
In: Journal of the Royal Statistical Society: Series A Statistics in Society, Vol. 169, No. 4, 01.10.2006, p. 681-700.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Lancaster, G, Green, M & Lane, S 2006, 'Reducing bias in ecological studies: an evaluation of different methodolgies.', Journal of the Royal Statistical Society: Series A Statistics in Society, vol. 169, no. 4, pp. 681-700. https://doi.org/10.1111/j.1467-985X.2006.00418.x

APA

Lancaster, G., Green, M., & Lane, S. (2006). Reducing bias in ecological studies: an evaluation of different methodolgies. Journal of the Royal Statistical Society: Series A Statistics in Society, 169(4), 681-700. https://doi.org/10.1111/j.1467-985X.2006.00418.x

Vancouver

Lancaster G, Green M, Lane S. Reducing bias in ecological studies: an evaluation of different methodolgies. Journal of the Royal Statistical Society: Series A Statistics in Society. 2006 Oct 1;169(4):681-700. doi: 10.1111/j.1467-985X.2006.00418.x

Author

Lancaster, Gillian ; Green, Mick ; Lane, Steven. / Reducing bias in ecological studies: an evaluation of different methodolgies. In: Journal of the Royal Statistical Society: Series A Statistics in Society. 2006 ; Vol. 169, No. 4. pp. 681-700.

Bibtex

@article{46ce380753f64923b2e1c6c371520ce8,
title = "Reducing bias in ecological studies: an evaluation of different methodolgies.",
abstract = "Summary. Statistical methods of ecological analysis that attempt to reduce ecological bias are empirically evaluated to determine in which circumstances each method might be practicable. The method that is most successful at reducing ecological bias is stratified ecological regression. It allows individual level covariate information to be incorporated into a stratified ecological analysis, as well as the combination of disease and risk factor information from two separate data sources, e.g. outcomes from a cancer registry and risk factor information from the census sample of anonymized records data set. The aggregated individual level model compares favourably with this model but has convergence problems. In addition, it is shown that the large areas that are covered by local authority districts seem to reduce between-area variability and may therefore not be as informative as conducting a ward level analysis. This has policy implications because access to ward level data is restricted.",
author = "Gillian Lancaster and Mick Green and Steven Lane",
note = "RAE_import_type : Journal article RAE_uoa_type : Statistics and Operational Research",
year = "2006",
month = oct,
day = "1",
doi = "10.1111/j.1467-985X.2006.00418.x",
language = "English",
volume = "169",
pages = "681--700",
journal = "Journal of the Royal Statistical Society: Series A Statistics in Society",
issn = "0964-1998",
publisher = "Wiley",
number = "4",

}

RIS

TY - JOUR

T1 - Reducing bias in ecological studies: an evaluation of different methodolgies.

AU - Lancaster, Gillian

AU - Green, Mick

AU - Lane, Steven

N1 - RAE_import_type : Journal article RAE_uoa_type : Statistics and Operational Research

PY - 2006/10/1

Y1 - 2006/10/1

N2 - Summary. Statistical methods of ecological analysis that attempt to reduce ecological bias are empirically evaluated to determine in which circumstances each method might be practicable. The method that is most successful at reducing ecological bias is stratified ecological regression. It allows individual level covariate information to be incorporated into a stratified ecological analysis, as well as the combination of disease and risk factor information from two separate data sources, e.g. outcomes from a cancer registry and risk factor information from the census sample of anonymized records data set. The aggregated individual level model compares favourably with this model but has convergence problems. In addition, it is shown that the large areas that are covered by local authority districts seem to reduce between-area variability and may therefore not be as informative as conducting a ward level analysis. This has policy implications because access to ward level data is restricted.

AB - Summary. Statistical methods of ecological analysis that attempt to reduce ecological bias are empirically evaluated to determine in which circumstances each method might be practicable. The method that is most successful at reducing ecological bias is stratified ecological regression. It allows individual level covariate information to be incorporated into a stratified ecological analysis, as well as the combination of disease and risk factor information from two separate data sources, e.g. outcomes from a cancer registry and risk factor information from the census sample of anonymized records data set. The aggregated individual level model compares favourably with this model but has convergence problems. In addition, it is shown that the large areas that are covered by local authority districts seem to reduce between-area variability and may therefore not be as informative as conducting a ward level analysis. This has policy implications because access to ward level data is restricted.

U2 - 10.1111/j.1467-985X.2006.00418.x

DO - 10.1111/j.1467-985X.2006.00418.x

M3 - Journal article

VL - 169

SP - 681

EP - 700

JO - Journal of the Royal Statistical Society: Series A Statistics in Society

JF - Journal of the Royal Statistical Society: Series A Statistics in Society

SN - 0964-1998

IS - 4

ER -