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In this setting, the most flexible approach makes use of a logistic regression model, typically assuming an additive effect of each allele on the disease (i.e. a multiplicative effect on the odds ratio).
We took two approaches for each side effect model: a multivariate logistic regression model that included all variables, and a machine learning logistic regression model with feature selection.
A multivariable logistic regression model assessed factors associated with delirium.
The use of a binary logistic regression model revealed two SNPs that are predictive of AIEC phenotype (Table 5).
The data were analysed using a mixed logistic regression model.
A multivariable logistic regression model was used to calculate ORs for various demographic and ECG characteristics.
A penalised logistic regression model was created for blonde colour, including all lead variants for blonde vs. black and brown hair colour.
NAFLD was determined by a previously described algorithm and a multivariable logistic regression model determined predictors of CVD.
A multivariable logistic regression model was estimated to identify factors associated with providing consent.
Risk factors were evaluated by a multivariate logistic regression model.
Hospital-specific 30-day RSMR was calculated using a hierarchical logistic regression model.
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