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Global odds model with proportional odds and trend odds applied to gross and microscopic brain infarcts

Global odds model with proportional odds and trend odds applied to gross and microscopic brain... Medical and epidemiological researchers commonly study ordinal measures of symptoms or pathology. Some of these studies involve two correlated ordinal measures. There is often an interest in including both measures in the modeling. It is common to see analyses that consider one of the measures as a predictor in the model for the other measure as an outcome. There are, however, issues with these analyses including a biased estimate of the probabilities and a decreased power due to multicollinearity (since they share some predictors). These issues create a necessity to examine both variables as simultaneous outcomes, by assessing the marginal probabilities for each outcome (i.e. using a proportional odds model) and the association between the two outcomes (i.e. using a constant global odds model). In this work, we extend this model using a parsimonious option when the constraints imposed by assumptions of proportional marginal odds and constant global odds do not hold. We compare approaches by using simulations and by analyzing data on brain infarcts in older adults. Age at death is a marginal predictor of gross infarcts and also a marginal predictor of microscopic infarcts, but does not modify the association between gross and microscopic infarcts. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biostatistics & Epidemiology Taylor & Francis

Global odds model with proportional odds and trend odds applied to gross and microscopic brain infarcts

Global odds model with proportional odds and trend odds applied to gross and microscopic brain infarcts

Abstract

Medical and epidemiological researchers commonly study ordinal measures of symptoms or pathology. Some of these studies involve two correlated ordinal measures. There is often an interest in including both measures in the modeling. It is common to see analyses that consider one of the measures as a predictor in the model for the other measure as an outcome. There are, however, issues with these analyses including a biased estimate of the probabilities and a decreased power due to...
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Publisher
Taylor & Francis
Copyright
© 2018 International Biometric Society – Chinese Region
ISSN
2470-9379
eISSN
2470-9360
DOI
10.1080/24709360.2018.1500089
Publisher site
See Article on Publisher Site

Abstract

Medical and epidemiological researchers commonly study ordinal measures of symptoms or pathology. Some of these studies involve two correlated ordinal measures. There is often an interest in including both measures in the modeling. It is common to see analyses that consider one of the measures as a predictor in the model for the other measure as an outcome. There are, however, issues with these analyses including a biased estimate of the probabilities and a decreased power due to multicollinearity (since they share some predictors). These issues create a necessity to examine both variables as simultaneous outcomes, by assessing the marginal probabilities for each outcome (i.e. using a proportional odds model) and the association between the two outcomes (i.e. using a constant global odds model). In this work, we extend this model using a parsimonious option when the constraints imposed by assumptions of proportional marginal odds and constant global odds do not hold. We compare approaches by using simulations and by analyzing data on brain infarcts in older adults. Age at death is a marginal predictor of gross infarcts and also a marginal predictor of microscopic infarcts, but does not modify the association between gross and microscopic infarcts.

Journal

Biostatistics & EpidemiologyTaylor & Francis

Published: Jul 26, 2018

Keywords: Global odds model; logistic regression; proportional odds; brain infarcts; Dale model

References