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Modelling high dimensional sets of binary co-morbidities

Date
2007
Abstract
The construction of classical co-morbidity indices is described. When the co-morbidities are binary we advocate the use of log-linear models which better capture the dependence structure in the data. We use R to implement new search strategies which enable us to analyse, sparse, high dimensional contingency tables rapidly and hence identify the best fitting models. We apply our new algorithms to a set of real medical data.
Supervisor
Description
peer-reviewed
Publisher
IWSM
Citation
Proceedings of the 22nd International Workshop on Statistical Modelling;
Collections
Mathematics & Statistics
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MacKenzie_2007_modelling.pdf
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Keywords
co-morbidity index, binary data, hierarchical long-linear model
ULRR Identifiers
https://hdl.handle.net/10344/2791
 https://doi.org/10.34961/4453
Funding code
Funding Information
Sustainable Development Goals
External Link
Type
Meetings and Proceedings
Rights
https://creativecommons.org/licenses/by-nc-sa/1.0/
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