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A comparative analysis on statistic performances and predictive model power was performed for the AM1 and DFT variants of CoMFA and CoMSIA models.

For the additive SNP model, power was 0.91 and the type I error was 0.04.

For the additive haplotype model, power was 0.94 and the type I error was 0.051.

Because of the low power of GEDT to detect the XOR model, power was re-evaluated for larger sample sizes.

Similarly, regression-based techniques to model power were reported in [4].

For the ACE A × E A × C model, power is good for a large effect size (0.92), power is acceptable for a medium effect size (0.81), and moderate for a small effect size (0.61).

Model power is variable because of the different number of classified heat wave days between HIs, due not only to the observed effect measure strength but also to the total number and geographical distribution of heat wave days.

For all GH Models power was consistently highest for detecting Interaction 1 and lowest for detecting both interactions; power to detect Interaction 2 was within 1% - 3% of that to detect Interaction 1 for all GH modes.

However, for data sets simulated under a pure-drift demographic model, this power was always lower (from 0.3% to 40.9%).

When adjusted for sex, age, fmc, smoking status, the glioma grade I risk significantly increased under additive genetic model(the power was only 0.743).

Evaluation of the model predictive power was achieved by comparing changes in the logical state of gene nodes with transcriptome data.