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Several test variables, include softening point, penetration, G∗/sin δ at 64 °C and rotational viscosity at 135 °C of three conventional binders were considered as dependent test factors (responses).
During the state dependent test, half of the birds received a shift in drug state from training to testing (Coc → Sal or Sal → Coc) while the other half remained in the same drug state (Coc → Coc or Sal → Sal).
In the patient dependent test set, 16 patients out of the whole patient set were selected.
The model used was a generalization of the Bayesian independence model to allow for dependent test outcomes.
The results that earned from patient dependent test set is shwon in Figure 3, Table 3 and Table 4.
In independent test set, the test patients don't appear in the training set, and the models achieved similar results as in patient dependent test set.
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This conservative performance is caused by the fact that the Bonferroni correction is used to adjust two dependent tests in SGWWS.
With the uncertainty of the disease allele, the overall p-value is then approximated from the two dependent tests.
The estimated FDR is computed from permutation of the data and allows for the possibility of dependent tests [ 3].
It is possible to assess pair-wise conditional dependence, i.e. assuming that one tests is conditionally independent given disease status of the other two potentially dependent tests.
Table 3 shows the results for the simulations involving conditionally dependent tests: true values as well as a summary of the estimated values across 1000 simulated datasets.
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