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Owing to the high number of comparisons, a Bonferroni correction was applied and symptoms significantly associated with each predictor were selected on the basis of having a p value less than 0.001.
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In the L1-norm penalized logistic regression, only one predictor is selected.
Finally, the most significant predictor is selected for segmenting the sample.
For DDE, one predictor was selected, the IgE of cat (OR = 1.378 per loge higher, p < 0.0001).
The best FP for each predictor was selected if it resulted in a significantly better fit (significantly smaller AIC) than the log transformation.
The best discriminating predictor is selected first, and then subsequent predictors are entered into the procedure if they contribute significantly to sub-typing cases that are homogeneous groups in terms of the value of the dependent variable.
For each method, we estimated the probability of selecting a true predictor as the proportion of cases where a true predictor was selected and the probability of not selecting a redundant variable as the proportion of cases where a redundant variable was not selected.
For each subset, the genes listed are those used in the minimum error rate predictor (except for the 'all' category, for which a seven-gene predictor was selected to observe a longer gene list to allow comparison with the other gene lists).
In the applied methodology, predictors were selected by an automated selection procedure.
In the case of MAGT, predictors with first and second order polynomial terms were included, whereas only linear predictors were selected for ALT modelling.
The final predictors were selected if p < 0.08.
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