Skip to main content

Table 1 Results of the Monte Carlo simulations

From: Optimum binary cut-off threshold of a diagnostic test: comparison of different methods using Monte Carlo technique

  N   Mutual information Youden Likelihood ratio
   Mean SD Mean Sd Mean SD
Scenario 1 (lognormal) 50 Cut-off 8.1 - 10.4 1.7 - 2.6 9.0 - 9.7 1.2 - 1.8 9.9 - 10.1 0.5 - 1.1
Se 0.78 - 0.86 0.17 - 0.30 0.82 - 0.91 0.10 - 0.14 0.74 - 0.76 0.08 - 0.16
Sp 0.69 - 0.76 0.18 - 0.23 0.73 - 0.82 0.12 - 0.17 0.78 - 0.78 0.06 - 0.14
100 Cut-off 8.1 - 10.0 1.4 - 2.2 9.2 - 9.5 1.0 - 1.4 10.0 - 10.10 0.4 - 0.7
Se 0.84 - 0.89 0.11 - 0.19 0.81 - 0.87 0.09 - 0.13 0.74 -0.75 0.05 - 0.11
Sp 0.65 - 0.74 0.15 - 0.22 0.72 - 0.78 0.09 - 0.15 0.78 - 0.78 0.04 - 0.10
200 Cut-off 7.8 - 9.7 1.1 - 1.9 9.2 - 9.4 0.8 - 1.1 10.0 - 10.1 0.3 - 0.5
Se 0.83 - 0.91 0.08 - 0.16 0.82 - 0.85 0.07 - 0.10 0.74 - 0.74 0.04 - 0.08
Sp 0.61 - 0.72 0.12 - 0.18 0.71 - 0.75 0.08 - 0.12 0.78 - 0.78 0.03 - 0.07
Scenario 2 (chi-square) 50 Cut-off 7.4 - 9.3 3.3 - 4.1 7.5 - 8.1 1.9 - 2.7 8.2 - 8.4 0.6 - 1.1
Se 0.67 - 0.70 0.26 - 0.35 0.69 - 0.80 0.17 - 90.21 0.59 - 0.61 0.08 - 0.17
Sp 0.62 - 0.73 0.24 - 0.30 0.65 - 0.77 0.17 - 0.22 0.69 - 0.70 0.07 - 0.15
100 Cut-off 7.4 - 9.0 3.1 - 4.1 7.5 - 8.0 1.5 - 2.2 8.3 - 8.4 0.4 - 0.8
Se 0.66 - 0.71 0.23 - 0.30 0.68 - 0.76 0.14 - 0.18 0.59 - 0.61 0.06 - 0.12
Sp 0.61 - 0.69 0.23 - 0.29 0.64 - 0.72 0.14 - 0.19 0.69 - 0.70 0.05 - 0.10
200 Cut-off 7.0 - 8.8 2.4 - 3.9 7.5 - 7.9 1.2 - 1.8 8.3 - 8.4 0.3 - 0.5
Se 0.67 - 0.72 0.20 - 0.27 0.67 - 0.72 0.12 - 0.16 0.59 - 0.60 0.04 - 0.09
Sp 0.59 - 0.65 0.21 - 0.26 0.63 - 0.67 0.12 - 0.17 0.69 - 0.70 0.03 - 0.07
Scenario 3 (inverse gamma) 50 Cut-off 5.1 - 7.1 1.7 - 2.6 5.1 - 5.8 0.9 - 1.6 5.4 - 5.8 0.5 - 0.9
Se 0.64 - 0.76 0.17 - 0.34 0.75 - 0.83 0.11 - 0.17 0.69 - 0.72 0.08 - 0.17
Sp 0.80 - 0.87 0.15 - 0.20 0.81 - 0.90 0.11 - 0.15 0.84 - 0.85 0.05 - 0.12
100 Cut-off 5.1 - 7.2 1.4 - 2.3 5.1 - 5.6 0.8 - 1.3 5.4 - 5.7 0.3 - 0.6
Se 0.63 - 0.76 0.15 - 0.27 0.75 - 0.79 0.09 - 0.14 0.69 - 0.71 0.06 - 0.12
Sp 0.81 - 0.87 0.12 - 0.19 0.81 - 0.85 0.09 - 0.13 0.84 - 0.85 0.04 - 0.08
200 Cut-off 5.1 - 7.2 1.1 - 2.2 5.1 - 5.3 0.6 - 1.0 5.5 - 5.6 0.2 - 0.5
Se 0.62 - 0.76 0.12 - 0.21 0.75 - 0.78 0.08 - 0.11 0.69 - 0.71 0.04 - 0.09
Sp 0.80 - 0.89 0.09 - 0.17 0.80 - 0.83 0.08 - 0.11 0.84 - 0.85 0.02 - 0.06
Scenario 4 (mixed) 50 Cut-off 9.9 - 14.2 1.6 - 2.5 10.4 - 14.1 1.6 - 2.9 12.1 - 14.6 1.1 - 1.8
Se 0.41 - 0.47 0.32 - 0.45 0.99 - 1.00 0.01 - 0.03 0.98 - 1.00 0.02 - 0.03
Sp 0.65 - 0.82 0.22 - 0.24 0.97 - 0.99 0.03 - 0.04 0.97 - 0.98 0.03 - 0.06
100 Cut-off 11.0 - 14.6 1.3 - 2.0 11.7 - 14.2 1.2 - 2.3 12.6 - 13.9 0.7 - 1.6
Se 0.48 - 0.69 0.43 - 0.47 0.98 - 1.00 0.02 - 0.02 0.98 - 0.99 0.02 - 0.03
Sp 0.79 - 0.94 0.13 - 0.23 0.97 - 0.98 0.02 - 0.04 0.97 - 0.97 0.02 - 0.05
200 Cut-off 11.4 - 14.6 1.0 - 1.4 12.5 - 13.9 0.9 - 1.7 13.1 - 13.6 0.5 - 1.2
Se 0.65 - 0.91 0.26 - 0.46 0.98 - 0.99 0.01 - 0.02 0.98 - 0.98 0.01 - 0.02
Sp 0.90 - 0.97 0.04 - 0.16 0.96 - 0.98 0.02 - 0.03 0.96 - 0.97 0.01 - 0.03
  1. For the 4 distributional scenarios and for total numbers of 50, 100 and 200 fictitious "individuals", the ranges of mean values and SD values, found by varying P(D) from 0.1 to 0.9 in steps of 0.1, of optimal cut-off limits and sensitivities and specificities are reported. Mean values and SD values are based on 1000 repetitions each.