Table 3.
Type-I-error for the NPDE-based model evaluation approach (positive-control simulation study)
Number of Subjects | Samples per Subject | Total Samples | Iterationsa | P-value (μNPDE)b | P-value (NPC)c |
---|---|---|---|---|---|
5 | 3 | 15 | 467 | 0.028 | 0.61 |
10 | 3 | 30 | 491 | 0.047 | 0.894 |
20 | 3 | 60 | 498 | 0.042 | 1 |
30 | 3 | 90 | 500 | 0.06 | 1 |
5 | 6 | 30 | 493 | 0.053 | 0.85 |
10 | 6 | 60 | 496 | 0.026 | 0.974 |
20 | 6 | 120 | 498 | 0.038 | 0.998 |
30 | 6 | 180 | 497 | 0.038 | 1 |
5 | 9 | 45 | 492 | 0.03 | 0.868 |
10 | 9 | 90 | 497 | 0.054 | 0.988 |
20 | 9 | 180 | 499 | 0.036 | 1 |
30 | 9 | 270 | 499 | 0.056 | 1 |
μNPDE mean of normalized prediction distribution errors; NPC, numerical predictive check
Iterations (out of 500) where the standard deviation of NPDE were not optimized to within ±0.01 of the target value (i.e., 1) by the developed fitting algorithm were excluded from the analysis.
Type-I-error for the NPDE-based model evaluation approach. Computed as the proportion of iterations where the μNPDE was statistically different than 0 (p-value<0.05; two-sided Student’s t-test).
Type-I-error for the conventional evaluation approach. Computed as the proportion of iterations where the proportion of observed data exceeding the model’s 90% prediction interval was statistically > 0.10 (p-value < 0.05; exact binomial test)