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Table 4 Differences between theoretic and estimated penetrance functions (sensitivity analysis: low minor allele frequency)

From: Artificial neural networks modeling gene-environment interaction

    High risk scenario    Low risk scenario  
   Neural network Logistic regression Logistic regression (DV) Neural network Logistic regression Logistic regression (DV)
    n = 1000 + 1000    n = 1000 + 1000  
  Genetic model 80.29 80.39 303.07 87.65 209.74 249.96
g u E g u Environmental model 79.60 278.32 277.18 78.18 170.94 170.94
Additive model 74.67 369.57 443.10 92.18 303.98 348.50
  Interaction model 180.02 415.60 541.02 191.77 327.44 481.62
  Model 1 113.62 244.87 375.43 179.23 226.03 355.59
g u E g u Model 2 232.75 389.70 472.47 318.57 346.57 460.08
Model 3 253.00 230.12 232.20 256.38 253.67 254.80
  Model 4 133.91 126.27 97.92 138.28 132.11 93.04
  1. Sum of mean absolute differences between theoretic and estimated penetrance function for 100 case-control data sets in the low and high risk scenario for different sample sizes. Bold numbers mark the best model fit comparing neural networks and logistic regression models. DV = design variables. Predictions were calculated for all models that do not have unspecified parameters due to empty cells.