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However, repeated-measures designs are not readily handled in standard logistic regression.
A hybrid model consisting of logistic regression model, Markov chain (MC), and cellular automata (CA) was designed to improve the performance of the standard logistic regression model.
Finally, three important extensions of standard logistic regression are briefly reviewed: conditional logistic regression, exact logistic regression, and logistic regression for clustered data.
Model comparison indicates that the full Bayesian random parameters logistic regression approach is statistically superior to the standard logistic regression model.
Our first classifier is a standard logistic regression classifier trained on tweets.
When standard logistic regression technique is employed for both equations log of odd ratio in favour of the event under consideration becomes the dependent variable in both equations.
Likelihood ratio (LR) test compares the given models with the corresponding models fit by standard logistic regression; its p-value is reported.
Once a set of PCs has been identified, they are adjusted with other covariates in a standard logistic regression model.
We chose log-binomial regression because it provides better estimates of the RR than does standard logistic regression when the prevalence of the outcome is greater than 10%.
The combination of catecholestrogen metabolism genotypes and sulfation was previously identified in breast cancer risk [30] using standard logistic regression methods.
Standard logistic regression analyses were performed for comparison purposes.
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