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Models were based on standard logistic regression and on penalized kernel-based algorithms (least squares support vector machines and kernel logistic regression).
The probit is based on standard normal distribution while the logit is based on standard logistic distribution.
Because borderline and metastatic tumors had low prevalence such that there was a risk of overfitting, we aimed to compare the performance of these approaches with those based on standard logistic regression analysis (without shrinkage).
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On the level of individual data points, the raw data residuals were within the normal range and the discriminant rule for classification of outcomes into events and non-events based on SEM Q-scores performed slightly worse but still similarly to the results based on standard approach using logistic regression.
If we applied pairwise coupling of 1-versus-1 logistic regression models based on standard stepwise variable selection with a p-value of 0.05 as the selection and removal threshold, we ended up with a total of 15 selected variables.
Hit selection was based on "standard scores".
Bacteria identification was based on standard methods.
The risk based on the standard logistic regression model was: P Y i = 1 | X i = 1 1 + exp − α ^ standard + ∑ m ∈ 1, …, 6 x im ⋅ β ^ standard, m where P Yi = 1) is the predicted risk that a patient i will get PONV, given patients' predictor values X.
Based on standards from US EPA, OECD and ASTM.
The parameters of the consensus model and the standard errors of these parameters based on the logistic regression model fit are reported in Appendix Fig S2.
As the number of bacteria initially used in each experiment cannot directly be obtained and consequently, death rates or survival rates cannot be directly computed; standard models based on logistic regression models to obtain LD values are thus not available.
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