Examples simplify understanding. Below is an example of how to use the theophylline dataset to generate NCA parameters.
## It is always a good idea to look at the data
head(datasets::Theoph)## Grouped Data: conc ~ Time | Subject
## Subject Wt Dose Time conc
## 1 1 79.6 4.02 0.00 0.74
## 2 1 79.6 4.02 0.25 2.84
## 3 1 79.6 4.02 0.57 6.57
## 4 1 79.6 4.02 1.12 10.50
## 5 1 79.6 4.02 2.02 9.66
## 6 1 79.6 4.02 3.82 8.58The columns that we will be interested in for our analysis are conc, Time, and Subject in the concentration data set and Dose, Time, and Subject for the dosing data set.
## By default it is groupedData; convert it to a data frame for use
my.conc <- PKNCAconc(as.data.frame(datasets::Theoph), conc~Time|Subject)
## Dosing data needs to only have one row per dose, so subset for
## that first.
d.dose <- unique(datasets::Theoph[datasets::Theoph$Time == 0,
c("Dose", "Time", "Subject")])
d.dose## Dose Time Subject
## 1 4.02 0 1
## 12 4.40 0 2
## 23 4.53 0 3
## 34 4.40 0 4
## 45 5.86 0 5
## 56 4.00 0 6
## 67 4.95 0 7
## 78 4.53 0 8
## 89 3.10 0 9
## 100 5.50 0 10
## 111 4.92 0 11
## 122 5.30 0 12my.dose <- PKNCAdose(d.dose, Dose~Time|Subject)After loading the data, they must be combined to prepare for parameter calculation. Intervals for calculation will automatically be selected based on the single.dose.aucs setting in PKNCA.options
my.data.automatic <- PKNCAdata(my.conc, my.dose)
PKNCA.options("single.dose.aucs")## start end auclast aucall aumclast aumcall cmax cmin tmax tlast tfirst
## 1 0 24 TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 2 0 Inf FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE
## clast.obs f cav ctrough ptr tlag half.life r.squared
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## adj.r.squared lambda.z lambda.z.time.first lambda.z.n.points clast.pred
## 1 FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE
## span.ratio aucinf aumcinf aucpext cl mrt vss vd thalf.eff
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## kel vz
## 1 FALSE FALSE
## 2 FALSE FALSEmy.data.automatic$intervals## start end auclast aucall aumclast aumcall cmax cmin tmax tlast
## 1 0 24 TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## adj.r.squared lambda.z lambda.z.time.first lambda.z.n.points clast.pred
## 1 FALSE FALSE FALSE FALSE FALSE
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## span.ratio aucinf aumcinf aucpext cl mrt vss vd thalf.eff
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## 24 FALSE FALSE 12Intervals for calculation can also be specified manually. Manual specification requires at least columns for start time, end time, and the parameters requested. The manual specification can also include any grouping factors from the concentration data set. Column order of the intervals is not important. When intervals are manually specified, they are expanded to the full interval set when added to a PKNCAdata object (in other words, a column is created for each parameter. Also, PKNCA automatically calculates parameters required for the NCA, so while lambda.z is required for calculating AUC0-\(\infinity\), you do not have to specify it in the parameters requested.
my.intervals <- data.frame(start=0,
end=Inf,
cmax=TRUE,
tmax=TRUE,
aucinf=TRUE,
auclast=TRUE)
my.data.manual <- PKNCAdata(my.conc, my.dose,
intervals=my.intervals)
my.data.manual$intervals## start end auclast aucall aumclast aumcall cmax cmin tmax tlast tfirst
## 1 0 Inf TRUE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE
## clast.obs f cav ctrough ptr tlag half.life r.squared
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## adj.r.squared lambda.z lambda.z.time.first lambda.z.n.points clast.pred
## 1 FALSE FALSE FALSE FALSE FALSE
## span.ratio aucinf aumcinf aucpext cl mrt vss vd thalf.eff
## 1 FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## kel vz
## 1 FALSE FALSEParameter calculation will automatically split the data by the grouping factor(s), subset by the interval, calculate all required parameters, record all options used for the calculations, and include data provenance to show that the calculation was performed as described. For all this, just call the pk.nca function with your PKNCAdata object.
my.results.automatic <- pk.nca(my.data.automatic)
head(my.results.automatic$result)## start end Subject PPTESTCD PPORRES
## 1 0 24 1 auclast 92.3654416
## 2 0 Inf 1 cmax 10.5000000
## 3 0 Inf 1 tmax 1.1200000
## 4 0 Inf 1 tlast 24.3700000
## 5 0 Inf 1 lambda.z 0.0484570
## 6 0 Inf 1 r.squared 0.9999997summary(my.results.automatic)| start | end | auclast | cmax | tmax | half.life | aucinf |
|---|---|---|---|---|---|---|
| 0 | 24 | 74.6 [24.3] | . | . | . | . |
| 0 | Inf | . | 8.65 [17.0] | 1.14 [0.630, 3.55] | 8.18 [2.12] | 115 [28.4] |
my.results.manual <- pk.nca(my.data.manual)
head(my.results.manual$result)## start end Subject PPTESTCD PPORRES
## 1 0 Inf 1 auclast 147.2347485
## 2 0 Inf 1 cmax 10.5000000
## 3 0 Inf 1 tmax 1.1200000
## 4 0 Inf 1 tlast 24.3700000
## 5 0 Inf 1 lambda.z 0.0484570
## 6 0 Inf 1 r.squared 0.9999997summary(my.results.manual)| start | end | auclast | cmax | tmax | aucinf |
|---|---|---|---|---|---|
| 0 | Inf | 98.7 [22.5] | 8.65 [17.0] | 1.14 [0.630, 3.55] | 115 [28.4] |