Hi,
I am trying to run Dose Proportionality in Pumas and have the following error. Can you please help with that.
These are the doses in my NCA dataset.
Row │ ID COHORT TIME DV route DOSE
│ Int64 Int64 Float64 Float64 String3 Int64
─────┼─────────────────────────────────────────────────
1 │ 1 1 0.0 0.0 ev 25
2 │ 7 2 0.0 0.0 ev 50
3 │ 12 3 0.0 0.0 ev 75
4 │ 16 4 0.0 0.0 ev 125
5 │ 20 5 0.0 0.0 ev 250
When I run NCA using run_nca funtion with sigdigits=2, the newly generated dose column (line 16-19) has a dose of 120 instead of the actual dose of 125. Conversely when I use sigdigits=3 it works fine and shows the dose in those line to be 125mg. Not sure why this discrepancy would arise by changing the significant digits which are decimal places?
Row │ id DOSE dose tlag tmax cmax tlast clast clast_pred auclast kel half_life aucinf_obs aucinf_pred vz_f_obs cl_f_obs vz_f_pred c ⋯ │ String Int64 Int64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 Float64 F ⋯─────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 1 │ 1 25 25 0.0 1.0 67.0 24.0 6.0 5.9 610.0 0.1 6.9 670.0 670.0 0.37 0.038 0.37 ⋯ 2 │ 2 25 25 0.0 3.0 79.0 24.0 15.0 15.0 1000.0 0.082 8.5 1200.0 1200.0 0.26 0.021 0.26
3 │ 3 25 25 0.0 2.0 75.0 24.0 7.4 7.3 760.0 0.1 6.7 830.0 830.0 0.29 0.03 0.29
4 │ 4 25 25 0.0 2.0 83.0 24.0 16.0 16.0 920.0 0.068 10.0 1200.0 1200.0 0.32 0.022 0.32
5 │ 5 25 25 0.0 2.0 94.0 24.0 26.0 26.0 1300.0 0.057 12.0 1700.0 1700.0 0.25 0.014 0.25 ⋯ 6 │ 6 25 25 0.0 2.0 110.0 24.0 18.0 18.0 1200.0 0.075 9.3 1500.0 1400.0 0.23 0.017 0.23
7 │ 7 50 50 0.0 4.0 190.0 24.0 37.0 36.0 2100.0 0.073 9.6 2600.0 2600.0 0.27 0.019 0.27
8 │ 8 50 50 0.0 1.0 160.0 24.0 17.0 17.0 1500.0 0.087 7.9 1600.0 1600.0 0.35 0.03 0.35
9 │ 9 50 50 0.0 1.0 190.0 24.0 35.0 35.0 2200.0 0.07 9.9 2700.0 2700.0 0.27 0.019 0.27 ⋯ 10 │ 10 50 50 0.0 3.0 170.0 24.0 24.0 24.0 1900.0 0.091 7.6 2200.0 2200.0 0.25 0.023 0.25
11 │ 11 50 50 0.0 2.0 160.0 24.0 19.0 19.0 1600.0 0.091 7.6 1800.0 1800.0 0.3 0.027 0.3
⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋱ 15 │ 15 75 75 0.0 4.0 290.0 24.0 89.0 88.0 4100.0 0.064 11.0 5500.0 5500.0 0.21 0.014 0.21
16 │ 16 125 120 0.0 0.75 390.0 24.0 47.0 46.0 4100.0 0.094 7.4 4600.0 4600.0 0.29 0.027 0.29 ⋯ 17 │ 17 125 120 0.0 1.0 520.0 24.0 80.0 79.0 5500.0 0.076 9.2 6600.0 6600.0 0.25 0.019 0.25
18 │ 18 125 120 0.0 1.0 520.0 24.0 130.0 130.0 6300.0 0.051 13.0 8800.0 8800.0 0.28 0.014 0.28
19 │ 19 125 120 0.0 2.0 540.0 24.0 140.0 140.0 7000.0 0.055 13.0 9500.0 9500.0 0.24 0.013 0.24
20 │ 20 250 250 0.0 3.0 680.0 24.0 97.0 97.0 7700.0 0.088 7.9 8900.0 8800.0 0.32 0.028 0.32 ⋯ 21 │ 21 250 250 0.0 2.0 820.0 24.0 170.0 170.0 9300.0 0.063 11.0 12000.0 12000.0 0.33 0.021 0.33
22 │ 22 250 250 0.0 3.0 910.0 24.0 230.0 240.0 12000.0 0.061 11.0 16000.0 16000.0 0.26 0.016 0.26
23 │ 23 250 250 0.0 2.0 750.0 24.0 130.0 130.0 8400.0 0.07 9.9 10000.0 10000.0 0.35 0.024 0.35
24 │ 24 250 250 0.0 0.75 1200.0 24.0 210.0 210.0 14000.0 0.068 10.0 17000.0 17000.0 0.22 0.015 0.22 ⋯ 25 │ 25 250 250 0.0 3.0 920.0 24.0 150.0 150.0 10000.0 0.073 9.5 12000.0 12000.0 0.28 0.02 0.28
This error then causes differences in the calculation of dose proportionality.
Dose linearity pairwise ratio test
Variable: cmax
────────────────────────────────────────────────────
Ratio Estimate low CI 90% high CI 90%
────────────────────────────────────────────────────
50 vs. 25 2.0 2.09008 1.7942 2.43475
75 vs. 25 3.0 3.48504 2.93812 4.13375
125 vs. 25 5.0 5.8609 4.83306 7.10732
250 vs. 25 10.0 10.3449 8.6096 12.4299
────────────────────────────────────────────────────
vs (wrong interpretation)
Dose linearity pairwise ratio test
Variable: cmax
────────────────────────────────────────────────────
Ratio Estimate low CI 90% high CI 90%
────────────────────────────────────────────────────
50 vs. 25 2.0 2.07577 1.77179 2.4319
75 vs. 25 3.0 3.45615 2.87823 4.1501
120 vs. 25 4.8 5.84529 4.78665 7.13806
250 vs. 25 10.0 10.3555 8.51974 12.5869
────────────────────────────────────────────────────