Computing NCA Parameters for Theophylline
Bill Denney
Source:vignettes/v02-example-theophylline.Rmd
v02-example-theophylline.Rmd
Examples simplify understanding. Below is an example of how to use the theophylline dataset to generate NCA parameters.
Load the data
Subject | Wt | Dose | Time | conc |
---|---|---|---|---|
1 | 79.6 | 4.02 | 0.00 | 0.74 |
1 | 79.6 | 4.02 | 0.25 | 2.84 |
1 | 79.6 | 4.02 | 0.57 | 6.57 |
1 | 79.6 | 4.02 | 1.12 | 10.50 |
1 | 79.6 | 4.02 | 2.02 | 9.66 |
1 | 79.6 | 4.02 | 3.82 | 8.58 |
The 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
conc_obj <- 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")])
knitr::kable(d_dose,
caption="Example dosing data extracted from theophylline data set")
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 | 12 |
dose_obj <- PKNCAdose(d_dose, Dose~Time|Subject)
Merge the Concentration and Dose
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
data_obj_automatic <- PKNCAdata(conc_obj, dose_obj)
knitr::kable(PKNCA.options("single.dose.aucs"))
start | end | auclast | aucall | aumclast | aumcall | aucint.last | aucint.last.dose | aucint.all | aucint.all.dose | c0 | cmax | cmin | tmax | tlast | tfirst | clast.obs | cl.last | cl.all | f | mrt.last | mrt.iv.last | vss.last | vss.iv.last | cav | cav.int.last | cav.int.all | ctrough | cstart | ptr | tlag | deg.fluc | swing | ceoi | aucabove.predose.all | aucabove.trough.all | count_conc | totdose | ae | clr.last | clr.obs | clr.pred | fe | sparse_auclast | sparse_auc_se | sparse_auc_df | time_above | aucivlast | aucivall | aucivint.last | aucivint.all | aucivpbextlast | aucivpbextall | aucivpbextint.last | aucivpbextint.all | half.life | r.squared | adj.r.squared | lambda.z | lambda.z.time.first | lambda.z.n.points | clast.pred | span.ratio | thalf.eff.last | thalf.eff.iv.last | kel.last | kel.iv.last | aucinf.obs | aucinf.pred | aumcinf.obs | aumcinf.pred | aucint.inf.obs | aucint.inf.obs.dose | aucint.inf.pred | aucint.inf.pred.dose | aucivinf.obs | aucivinf.pred | aucivpbextinf.obs | aucivpbextinf.pred | aucpext.obs | aucpext.pred | cl.obs | cl.pred | mrt.obs | mrt.pred | mrt.iv.obs | mrt.iv.pred | mrt.md.obs | mrt.md.pred | vz.obs | vz.pred | vss.obs | vss.pred | vss.iv.obs | vss.iv.pred | vss.md.obs | vss.md.pred | cav.int.inf.obs | cav.int.inf.pred | thalf.eff.obs | thalf.eff.pred | thalf.eff.iv.obs | thalf.eff.iv.pred | kel.obs | kel.pred | kel.iv.obs | kel.iv.pred | auclast.dn | aucall.dn | aucinf.obs.dn | aucinf.pred.dn | aumclast.dn | aumcall.dn | aumcinf.obs.dn | aumcinf.pred.dn | cmax.dn | cmin.dn | clast.obs.dn | clast.pred.dn | cav.dn | ctrough.dn |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 24 | TRUE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
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knitr::kable(data_obj_automatic$intervals)
start | end | auclast | aucall | aumclast | aumcall | aucint.last | aucint.last.dose | aucint.all | aucint.all.dose | c0 | cmax | cmin | tmax | tlast | tfirst | clast.obs | cl.last | cl.all | f | mrt.last | mrt.iv.last | vss.last | vss.iv.last | cav | cav.int.last | cav.int.all | ctrough | cstart | ptr | tlag | deg.fluc | swing | ceoi | aucabove.predose.all | aucabove.trough.all | count_conc | totdose | ae | clr.last | clr.obs | clr.pred | fe | sparse_auclast | sparse_auc_se | sparse_auc_df | time_above | aucivlast | aucivall | aucivint.last | aucivint.all | aucivpbextlast | aucivpbextall | aucivpbextint.last | aucivpbextint.all | half.life | r.squared | adj.r.squared | lambda.z | lambda.z.time.first | lambda.z.n.points | clast.pred | span.ratio | thalf.eff.last | thalf.eff.iv.last | kel.last | kel.iv.last | aucinf.obs | aucinf.pred | aumcinf.obs | aumcinf.pred | aucint.inf.obs | aucint.inf.obs.dose | aucint.inf.pred | aucint.inf.pred.dose | aucivinf.obs | aucivinf.pred | aucivpbextinf.obs | aucivpbextinf.pred | aucpext.obs | aucpext.pred | cl.obs | cl.pred | mrt.obs | mrt.pred | mrt.iv.obs | mrt.iv.pred | mrt.md.obs | mrt.md.pred | vz.obs | vz.pred | vss.obs | vss.pred | vss.iv.obs | vss.iv.pred | vss.md.obs | vss.md.pred | cav.int.inf.obs | cav.int.inf.pred | thalf.eff.obs | thalf.eff.pred | thalf.eff.iv.obs | thalf.eff.iv.pred | kel.obs | kel.pred | kel.iv.obs | kel.iv.pred | auclast.dn | aucall.dn | aucinf.obs.dn | aucinf.pred.dn | aumclast.dn | aumcall.dn | aumcinf.obs.dn | aumcinf.pred.dn | cmax.dn | cmin.dn | clast.obs.dn | clast.pred.dn | cav.dn | ctrough.dn | Subject |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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0 | Inf | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | TRUE | FALSE | TRUE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | TRUE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | TRUE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | 10 |
0 | 24 | TRUE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | 11 |
0 | Inf | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | TRUE | FALSE | TRUE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | TRUE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | TRUE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | 11 |
0 | 24 | TRUE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | 12 |
0 | Inf | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | TRUE | FALSE | TRUE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | TRUE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | TRUE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | 12 |
Intervals 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-,
you do not have to specify it in the parameters requested.
intervals_manual <- data.frame(start=0,
end=Inf,
cmax=TRUE,
tmax=TRUE,
aucinf.obs=TRUE,
auclast=TRUE)
data_obj_manual <- PKNCAdata(conc_obj, dose_obj,
intervals=intervals_manual)
knitr::kable(data_obj_manual$intervals)
start | end | auclast | aucall | aumclast | aumcall | aucint.last | aucint.last.dose | aucint.all | aucint.all.dose | c0 | cmax | cmin | tmax | tlast | tfirst | clast.obs | cl.last | cl.all | f | mrt.last | mrt.iv.last | vss.last | vss.iv.last | cav | cav.int.last | cav.int.all | ctrough | cstart | ptr | tlag | deg.fluc | swing | ceoi | aucabove.predose.all | aucabove.trough.all | count_conc | totdose | ae | clr.last | clr.obs | clr.pred | fe | sparse_auclast | sparse_auc_se | sparse_auc_df | time_above | aucivlast | aucivall | aucivint.last | aucivint.all | aucivpbextlast | aucivpbextall | aucivpbextint.last | aucivpbextint.all | half.life | r.squared | adj.r.squared | lambda.z | lambda.z.time.first | lambda.z.n.points | clast.pred | span.ratio | thalf.eff.last | thalf.eff.iv.last | kel.last | kel.iv.last | aucinf.obs | aucinf.pred | aumcinf.obs | aumcinf.pred | aucint.inf.obs | aucint.inf.obs.dose | aucint.inf.pred | aucint.inf.pred.dose | aucivinf.obs | aucivinf.pred | aucivpbextinf.obs | aucivpbextinf.pred | aucpext.obs | aucpext.pred | cl.obs | cl.pred | mrt.obs | mrt.pred | mrt.iv.obs | mrt.iv.pred | mrt.md.obs | mrt.md.pred | vz.obs | vz.pred | vss.obs | vss.pred | vss.iv.obs | vss.iv.pred | vss.md.obs | vss.md.pred | cav.int.inf.obs | cav.int.inf.pred | thalf.eff.obs | thalf.eff.pred | thalf.eff.iv.obs | thalf.eff.iv.pred | kel.obs | kel.pred | kel.iv.obs | kel.iv.pred | auclast.dn | aucall.dn | aucinf.obs.dn | aucinf.pred.dn | aumclast.dn | aumcall.dn | aumcinf.obs.dn | aumcinf.pred.dn | cmax.dn | cmin.dn | clast.obs.dn | clast.pred.dn | cav.dn | ctrough.dn |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | Inf | TRUE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | TRUE | FALSE | TRUE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | TRUE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
Compute the parameters
Parameter 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.
results_obj_automatic <- pk.nca(data_obj_automatic)
knitr::kable(head(as.data.frame(results_obj_automatic)))
Subject | start | end | PPTESTCD | PPORRES | exclude |
---|---|---|---|---|---|
1 | 0 | 24 | auclast | 92.365442 | NA |
1 | 0 | Inf | cmax | 10.500000 | NA |
1 | 0 | Inf | tmax | 1.120000 | NA |
1 | 0 | Inf | tlast | 24.370000 | NA |
1 | 0 | Inf | clast.obs | 3.280000 | NA |
1 | 0 | Inf | lambda.z | 0.048457 | NA |
summary(results_obj_automatic)
start | end | N | auclast | cmax | tmax | half.life | aucinf.obs |
---|---|---|---|---|---|---|---|
0 | 24 | 12 | 74.6 [24.3] | . | . | . | . |
0 | Inf | 12 | . | 8.65 [17.0] | 1.14 [0.630, 3.55] | 8.18 [2.12] | 115 [28.4] |
results_obj_manual <- pk.nca(data_obj_manual)
knitr::kable(head(as.data.frame(results_obj_manual)))
Subject | start | end | PPTESTCD | PPORRES | exclude |
---|---|---|---|---|---|
6 | 0 | Inf | auclast | 71.6970150 | NA |
6 | 0 | Inf | cmax | 6.4400000 | NA |
6 | 0 | Inf | tmax | 1.1500000 | NA |
6 | 0 | Inf | tlast | 23.8500000 | NA |
6 | 0 | Inf | clast.obs | 0.9200000 | NA |
6 | 0 | Inf | lambda.z | 0.0877957 | NA |
summary(results_obj_manual)
start | end | N | auclast | cmax | tmax | aucinf.obs |
---|---|---|---|---|---|---|
0 | Inf | 12 | 98.7 [22.5] | 8.65 [17.0] | 1.14 [0.630, 3.55] | 115 [28.4] |
Multiple Dose Example
Assessing multiple dose pharmacokinetics is conceptually the same as single-dose in PKNCA.
To assess multiple dose PK, the theophylline data will be extended from single to multiple doses using superposition (see the superposition vignette for more information).
d_conc <- PKNCAconc(as.data.frame(Theoph), conc~Time|Subject)
conc_obj_multi <-
PKNCAconc(
superposition(d_conc,
tau=168,
dose.times=seq(0, 144, by=24),
n.tau=1,
check.blq=FALSE),
conc~time|Subject)
conc_obj_multi
## Formula for concentration:
## conc ~ time | Subject
## Data are dense PK.
## With 12 subjects defined in the 'Subject' column.
## Nominal time column is not specified.
##
## First 6 rows of concentration data:
## Subject conc time exclude volume duration
## 1 0.74000 0.00 <NA> NA 0
## 1 2.84000 0.25 <NA> NA 0
## 1 4.23875 0.37 <NA> NA 0
## 1 6.57000 0.57 <NA> NA 0
## 1 10.50000 1.12 <NA> NA 0
## 1 9.66000 2.02 <NA> NA 0
dose_obj_multi <- PKNCAdose(expand.grid(Subject=unique(as.data.frame(conc_obj_multi)$Subject),
time=seq(0, 144, by=24)),
~time|Subject)
dose_obj_multi
## Formula for dosing:
## ~time | Subject
## Nominal time column is not specified.
##
## First 6 rows of dosing data:
## Subject time exclude route duration
## 1 0 <NA> extravascular 0
## 2 0 <NA> extravascular 0
## 3 0 <NA> extravascular 0
## 4 0 <NA> extravascular 0
## 5 0 <NA> extravascular 0
## 6 0 <NA> extravascular 0
The superposition-simulated scenario is not especially realistic as it includes dense sampling on every day. With this scenario, the intervals automatically selected have an interval for every subject on every day.
data_obj <- PKNCAdata(conc_obj_multi, dose_obj_multi)
data_obj$intervals[,c("Subject", "start", "end")]
## # A tibble: 84 × 3
## Subject start end
## <ord> <dbl> <dbl>
## 1 1 0 24
## 2 1 24 48
## 3 1 48 72
## 4 1 72 96
## 5 1 96 120
## 6 1 120 144
## 7 1 144 168
## 8 2 0 24
## 9 2 24 48
## 10 2 48 72
## # ℹ 74 more rows
In a more realistic scenario, dense PK sampling occurs for every
subject on the first and last days. To select those intervals manually,
specify the intervals of interest in the intervals
argument
to the PKNCAdata function call. The intervals are automatically expanded
not to calculate anything that was not requested.
intervals_manual <- data.frame(start=c(0, 144),
end=c(24, 168),
cmax=TRUE,
auclast=TRUE)
data_obj <- PKNCAdata(conc_obj_multi, dose_obj_multi,
intervals=intervals_manual)
data_obj$intervals
## start end auclast aucall aumclast aumcall aucint.last aucint.last.dose
## 1 0 24 TRUE FALSE FALSE FALSE FALSE FALSE
## 2 144 168 TRUE FALSE FALSE FALSE FALSE FALSE
## aucint.all aucint.all.dose c0 cmax cmin tmax tlast tfirst clast.obs
## 1 FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## cl.last cl.all f mrt.last mrt.iv.last vss.last vss.iv.last cav
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## cav.int.last cav.int.all ctrough cstart ptr tlag deg.fluc swing ceoi
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## aucabove.predose.all aucabove.trough.all count_conc totdose ae clr.last
## 1 FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE FALSE
## clr.obs clr.pred fe sparse_auclast sparse_auc_se sparse_auc_df time_above
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## aucivlast aucivall aucivint.last aucivint.all aucivpbextlast aucivpbextall
## 1 FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE FALSE
## aucivpbextint.last aucivpbextint.all half.life r.squared adj.r.squared
## 1 FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE
## lambda.z lambda.z.time.first lambda.z.n.points clast.pred span.ratio
## 1 FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE
## thalf.eff.last thalf.eff.iv.last kel.last kel.iv.last aucinf.obs aucinf.pred
## 1 FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE FALSE
## aumcinf.obs aumcinf.pred aucint.inf.obs aucint.inf.obs.dose aucint.inf.pred
## 1 FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE
## aucint.inf.pred.dose aucivinf.obs aucivinf.pred aucivpbextinf.obs
## 1 FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE
## aucivpbextinf.pred aucpext.obs aucpext.pred cl.obs cl.pred mrt.obs mrt.pred
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## mrt.iv.obs mrt.iv.pred mrt.md.obs mrt.md.pred vz.obs vz.pred vss.obs vss.pred
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## vss.iv.obs vss.iv.pred vss.md.obs vss.md.pred cav.int.inf.obs
## 1 FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE
## cav.int.inf.pred thalf.eff.obs thalf.eff.pred thalf.eff.iv.obs
## 1 FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE
## thalf.eff.iv.pred kel.obs kel.pred kel.iv.obs kel.iv.pred auclast.dn
## 1 FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE FALSE
## aucall.dn aucinf.obs.dn aucinf.pred.dn aumclast.dn aumcall.dn aumcinf.obs.dn
## 1 FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE FALSE
## aumcinf.pred.dn cmax.dn cmin.dn clast.obs.dn clast.pred.dn cav.dn ctrough.dn
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE FALSE FALSE
After the data is ready, the calculations and summary can progress.
## $result
## # A tibble: 48 × 6
## Subject start end PPTESTCD PPORRES exclude
## <ord> <dbl> <dbl> <chr> <dbl> <chr>
## 1 6 0 24 auclast 71.8 NA
## 2 6 0 24 cmax 6.44 NA
## 3 6 144 168 auclast 82.2 NA
## 4 6 144 168 cmax 7.37 NA
## 5 7 0 24 auclast 89.0 NA
## 6 7 0 24 cmax 7.09 NA
## 7 7 144 168 auclast 101. NA
## 8 7 144 168 cmax 8.07 NA
## 9 8 0 24 auclast 86.7 NA
## 10 8 0 24 cmax 7.56 NA
## # ℹ 38 more rows
##
## $data
## Formula for concentration:
## conc ~ time | Subject
## Data are dense PK.
## With 12 subjects defined in the 'Subject' column.
## Nominal time column is not specified.
##
## First 6 rows of concentration data:
## Subject conc time exclude volume duration
## 1 0.74000 0.00 <NA> NA 0
## 1 2.84000 0.25 <NA> NA 0
## 1 4.23875 0.37 <NA> NA 0
## 1 6.57000 0.57 <NA> NA 0
## 1 10.50000 1.12 <NA> NA 0
## 1 9.66000 2.02 <NA> NA 0
## Formula for dosing:
## ~time | Subject
## Nominal time column is not specified.
##
## First 6 rows of dosing data:
## Subject time exclude route duration
## 1 0 <NA> extravascular 0
## 2 0 <NA> extravascular 0
## 3 0 <NA> extravascular 0
## 4 0 <NA> extravascular 0
## 5 0 <NA> extravascular 0
## 6 0 <NA> extravascular 0
##
## With 2 rows of interval specifications.
## With imputation: NA
## Options changed from default are:
## $adj.r.squared.factor
## [1] 1e-04
##
## $max.missing
## [1] 0.5
##
## $auc.method
## [1] "lin up/log down"
##
## $conc.na
## [1] "drop"
##
## $conc.blq
## $conc.blq$first
## [1] "keep"
##
## $conc.blq$middle
## [1] "drop"
##
## $conc.blq$last
## [1] "keep"
##
##
## $first.tmax
## [1] TRUE
##
## $allow.tmax.in.half.life
## [1] FALSE
##
## $keep_interval_cols
## NULL
##
## $min.hl.points
## [1] 3
##
## $min.span.ratio
## [1] 2
##
## $max.aucinf.pext
## [1] 20
##
## $min.hl.r.squared
## [1] 0.9
##
## $progress
## [1] TRUE
##
## $tau.choices
## [1] NA
##
## $single.dose.aucs
## start end auclast aucall aumclast aumcall aucint.last aucint.last.dose
## 1 0 24 TRUE FALSE FALSE FALSE FALSE FALSE
## 2 0 Inf FALSE FALSE FALSE FALSE FALSE FALSE
## aucint.all aucint.all.dose c0 cmax cmin tmax tlast tfirst clast.obs
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE
## cl.last cl.all f mrt.last mrt.iv.last vss.last vss.iv.last cav
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## cav.int.last cav.int.all ctrough cstart ptr tlag deg.fluc swing ceoi
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## aucabove.predose.all aucabove.trough.all count_conc totdose ae clr.last
## 1 FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE FALSE
## clr.obs clr.pred fe sparse_auclast sparse_auc_se sparse_auc_df time_above
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## aucivlast aucivall aucivint.last aucivint.all aucivpbextlast aucivpbextall
## 1 FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE FALSE
## aucivpbextint.last aucivpbextint.all half.life r.squared adj.r.squared
## 1 FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE TRUE FALSE FALSE
## lambda.z lambda.z.time.first lambda.z.n.points clast.pred span.ratio
## 1 FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE
## thalf.eff.last thalf.eff.iv.last kel.last kel.iv.last aucinf.obs aucinf.pred
## 1 FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE TRUE FALSE
## aumcinf.obs aumcinf.pred aucint.inf.obs aucint.inf.obs.dose aucint.inf.pred
## 1 FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE
## aucint.inf.pred.dose aucivinf.obs aucivinf.pred aucivpbextinf.obs
## 1 FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE
## aucivpbextinf.pred aucpext.obs aucpext.pred cl.obs cl.pred mrt.obs mrt.pred
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## mrt.iv.obs mrt.iv.pred mrt.md.obs mrt.md.pred vz.obs vz.pred vss.obs vss.pred
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## vss.iv.obs vss.iv.pred vss.md.obs vss.md.pred cav.int.inf.obs
## 1 FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE
## cav.int.inf.pred thalf.eff.obs thalf.eff.pred thalf.eff.iv.obs
## 1 FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE
## thalf.eff.iv.pred kel.obs kel.pred kel.iv.obs kel.iv.pred auclast.dn
## 1 FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE FALSE
## aucall.dn aucinf.obs.dn aucinf.pred.dn aumclast.dn aumcall.dn aumcinf.obs.dn
## 1 FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE FALSE
## aumcinf.pred.dn cmax.dn cmin.dn clast.obs.dn clast.pred.dn cav.dn ctrough.dn
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##
##
## $columns
## $columns$exclude
## [1] "exclude"
##
##
## attr(,"class")
## [1] "PKNCAresults" "list"
## attr(,"provenance")
## Provenance hash ee687a520af821aa354fef62aa53cba0 generated on 2024-12-07 20:33:05.087509 with R version 4.4.2 (2024-10-31).
summary(results_obj)
## start end N auclast cmax
## 0 24 12 98.8 [23.0] 8.65 [17.0]
## 144 168 12 115 [28.4] 10.0 [21.0]
##
## Caption: auclast, cmax: geometric mean and geometric coefficient of variation; N: number of subjects