Competing risk regression, with Fine and Gray models, yielded smaller hazard ratios than traditional Cox models for all associations except for cerebrovascular disease (table 4).
In this setting, the LVD model performs like the ES model while the GR and GVD models yield small occurrence probabilities for large lists.
In addition, the segregation models generally yielded smaller P-values for association than those obtained through the logistic regression analysis.
The identified Multi-Layer Perceptron (MLP) neural network model yields small verification error compared with that of the adaptive Radial Base Function (RBF) neural network model.
First, that the analysis of the combinatorial library data in terms of an 'independent mutation effect model' yields small values for the mutation effects on activity and catalysis (Supplementary Figure S5 at http://www.BiochemJ.org/bj/429/bj4290243add.htm).htm
Compared to the other peak models, the mAPV model yielded much smaller mean percentage errors of 2.47%, 2.33% and 2.26% for symmetric (μ = 1), moderately asymmetric (μ = 1.5) and highly asymmetric (μ = 2) peaks respectively.
Imputing data using regression-based methods necessarily biases efficacy estimates toward the null, so it is not surprising that the fourth model yielded the smallest efficacy estimate of 2.3 fewer carious surfaces, on average, per child.
This relatively modest, but reliable, difference in BIC reflected an inherent difficulty in distinguishing these models with the particular task conditions we used (fitting simulated data from either model yielded similarly small BIC differences: −1.448 ± 0.900 for simulations based on the normative fits and 1.393 ± 1.054 for simulations using the stabilizing-boundary fits).
As first developed, CART, could lead to quite large tree models, but recent work has incorporated p-value based tree modelling, known as conditional classification trees, which yield smaller tree models whilst simultaneously controlling for multiple testing and are available in the Party Package [ 23] in R [ 24].
