Examples simplify understanding. Below is an example of how to use the theophylline dataset to generate NCA parameters.

Load the data

## It is always a good idea to look at the data
knitr::kable(head(datasets::Theoph))

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")

Example dosing data extracted from theophylline data set
	Dose	Subject
1	4.02	1
12	4.40	2
23	4.53	3
34	4.40	4
45	5.86	5
56	4.00	6
67	4.95	7
78	4.53	8
89	3.10	9
100	5.50	10
111	4.92	11
122	5.30	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	auclast.dn	aucall.dn	aumclast.dn	aumcall.dn	cmax	cmin	tmax	tlast	tfirst	clast.obs	cl.last	cl.all	f	mrt.last	mrt.iv.last	vss.last	vss.iv.last	cav	ctrough	ptr	tlag	deg.fluc	swing	ceoi	ae	clr.last	clr.obs	clr.pred	fe	half.life	r.squared	adj.r.squared	lambda.z	lambda.z.time.first	lambda.z.n.points	clast.pred	span.ratio	cmax.dn	cmin.dn	clast.obs.dn	clast.pred.dn	cav.dn	ctrough.dn	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	aucinf.obs.dn	aucinf.pred.dn	aumcinf.obs.dn	aumcinf.pred.dn	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	vd.obs	vd.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
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
0	Inf	FALSE	FALSE	FALSE	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	TRUE	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

knitr::kable(data_obj_automatic$intervals)

end	auclast	aucall	aumclast	aumcall	aucint.last	aucint.last.dose	aucint.all	aucint.all.dose	auclast.dn	aucall.dn	aumclast.dn	aumcall.dn	cmax	cmin	tmax	tlast	tfirst	clast.obs	cl.last	cl.all	f	mrt.last	mrt.iv.last	vss.last	vss.iv.last	cav	ctrough	ptr	tlag	deg.fluc	swing	ceoi	ae	clr.last	clr.obs	clr.pred	fe	half.life	r.squared	adj.r.squared	lambda.z	lambda.z.time.first	lambda.z.n.points	clast.pred	span.ratio	cmax.dn	cmin.dn	clast.obs.dn	clast.pred.dn	cav.dn	ctrough.dn	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	aucinf.obs.dn	aucinf.pred.dn	aumcinf.obs.dn	aumcinf.pred.dn	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	vd.obs	vd.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	Subject
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	1
Inf	FALSE	FALSE	FALSE	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	TRUE	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	1
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	2
Inf	FALSE	FALSE	FALSE	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	TRUE	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	2
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	3
Inf	FALSE	FALSE	FALSE	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	TRUE	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	3
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	4
Inf	FALSE	FALSE	FALSE	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	TRUE	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	4
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	5
Inf	FALSE	FALSE	FALSE	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	TRUE	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	5
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	6
Inf	FALSE	FALSE	FALSE	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	TRUE	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	6
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	7
Inf	FALSE	FALSE	FALSE	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	TRUE	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	7
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	8
Inf	FALSE	FALSE	FALSE	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	TRUE	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	8
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	9
Inf	FALSE	FALSE	FALSE	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	TRUE	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	9
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	10
Inf	FALSE	FALSE	FALSE	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	TRUE	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	10
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	11
Inf	FALSE	FALSE	FALSE	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	TRUE	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	11
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	12
Inf	FALSE	FALSE	FALSE	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	TRUE	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	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 AUC_{0-\(\infinity\)}, 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	auclast.dn	aucall.dn	aumclast.dn	aumcall.dn	cmax	cmin	tmax	tlast	tfirst	clast.obs	cl.last	cl.all	f	mrt.last	mrt.iv.last	vss.last	vss.iv.last	cav	ctrough	ptr	tlag	deg.fluc	swing	ceoi	ae	clr.last	clr.obs	clr.pred	fe	half.life	r.squared	adj.r.squared	lambda.z	lambda.z.time.first	lambda.z.n.points	clast.pred	span.ratio	cmax.dn	cmin.dn	clast.obs.dn	clast.pred.dn	cav.dn	ctrough.dn	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	aucinf.obs.dn	aucinf.pred.dn	aumcinf.obs.dn	aumcinf.pred.dn	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	vd.obs	vd.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
0	Inf	TRUE	FALSE	FALSE	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	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

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)))

end	Subject	PPTESTCD	PPORRES	exclude
24	1	auclast	92.365442	NA
Inf	1	cmax	10.500000	NA
Inf	1	tmax	1.120000	NA
Inf	1	tlast	24.370000	NA
Inf	1	clast.obs	3.280000	NA
Inf	1	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)))

end	Subject	PPTESTCD	PPORRES	exclude
Inf	1	auclast	147.234748	NA
Inf	1	cmax	10.500000	NA
Inf	1	tmax	1.120000	NA
Inf	1	tlast	24.370000	NA
Inf	1	clast.obs	3.280000	NA
Inf	1	lambda.z	0.048457	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
## 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(conc_obj_multi$data$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")]

##    Subject start end
## 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
## 11       2    72  96
## 12       2    96 120
## 13       2   120 144
## 14       2   144 168
## 15       3     0  24
## 16       3    24  48
## 17       3    48  72
## 18       3    72  96
## 19       3    96 120
## 20       3   120 144
## 21       3   144 168
## 22       4     0  24
## 23       4    24  48
## 24       4    48  72
## 25       4    72  96
## 26       4    96 120
## 27       4   120 144
## 28       4   144 168
## 29       5     0  24
## 30       5    24  48
## 31       5    48  72
## 32       5    72  96
## 33       5    96 120
## 34       5   120 144
## 35       5   144 168
## 36       6     0  24
## 37       6    24  48
## 38       6    48  72
## 39       6    72  96
## 40       6    96 120
## 41       6   120 144
## 42       6   144 168
## 43       7     0  24
## 44       7    24  48
## 45       7    48  72
## 46       7    72  96
## 47       7    96 120
## 48       7   120 144
## 49       7   144 168
## 50       8     0  24
## 51       8    24  48
## 52       8    48  72
## 53       8    72  96
## 54       8    96 120
## 55       8   120 144
## 56       8   144 168
## 57       9     0  24
## 58       9    24  48
## 59       9    48  72
## 60       9    72  96
## 61       9    96 120
## 62       9   120 144
## 63       9   144 168
## 64      10     0  24
## 65      10    24  48
## 66      10    48  72
## 67      10    72  96
## 68      10    96 120
## 69      10   120 144
## 70      10   144 168
## 71      11     0  24
## 72      11    24  48
## 73      11    48  72
## 74      11    72  96
## 75      11    96 120
## 76      11   120 144
## 77      11   144 168
## 78      12     0  24
## 79      12    24  48
## 80      12    48  72
## 81      12    72  96
## 82      12    96 120
## 83      12   120 144
## 84      12   144 168

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 auclast.dn aucall.dn aumclast.dn aumcall.dn cmax
## 1      FALSE           FALSE      FALSE     FALSE       FALSE      FALSE TRUE
## 2      FALSE           FALSE      FALSE     FALSE       FALSE      FALSE TRUE
##    cmin  tmax tlast tfirst clast.obs cl.last cl.all     f mrt.last mrt.iv.last
## 1 FALSE FALSE FALSE  FALSE     FALSE   FALSE  FALSE FALSE    FALSE       FALSE
## 2 FALSE FALSE FALSE  FALSE     FALSE   FALSE  FALSE FALSE    FALSE       FALSE
##   vss.last vss.iv.last   cav ctrough   ptr  tlag deg.fluc swing  ceoi    ae
## 1    FALSE       FALSE FALSE   FALSE FALSE FALSE    FALSE FALSE FALSE FALSE
## 2    FALSE       FALSE FALSE   FALSE FALSE FALSE    FALSE FALSE FALSE FALSE
##   clr.last clr.obs clr.pred    fe half.life r.squared adj.r.squared lambda.z
## 1    FALSE   FALSE    FALSE FALSE     FALSE     FALSE         FALSE    FALSE
## 2    FALSE   FALSE    FALSE FALSE     FALSE     FALSE         FALSE    FALSE
##   lambda.z.time.first lambda.z.n.points clast.pred span.ratio cmax.dn cmin.dn
## 1               FALSE             FALSE      FALSE      FALSE   FALSE   FALSE
## 2               FALSE             FALSE      FALSE      FALSE   FALSE   FALSE
##   clast.obs.dn clast.pred.dn cav.dn ctrough.dn thalf.eff.last thalf.eff.iv.last
## 1        FALSE         FALSE  FALSE      FALSE          FALSE             FALSE
## 2        FALSE         FALSE  FALSE      FALSE          FALSE             FALSE
##   kel.last kel.iv.last aucinf.obs aucinf.pred aumcinf.obs aumcinf.pred
## 1    FALSE       FALSE      FALSE       FALSE       FALSE        FALSE
## 2    FALSE       FALSE      FALSE       FALSE       FALSE        FALSE
##   aucint.inf.obs aucint.inf.obs.dose aucint.inf.pred aucint.inf.pred.dose
## 1          FALSE               FALSE           FALSE                FALSE
## 2          FALSE               FALSE           FALSE                FALSE
##   aucinf.obs.dn aucinf.pred.dn aumcinf.obs.dn aumcinf.pred.dn aucpext.obs
## 1         FALSE          FALSE          FALSE           FALSE       FALSE
## 2         FALSE          FALSE          FALSE           FALSE       FALSE
##   aucpext.pred cl.obs cl.pred mrt.obs mrt.pred mrt.iv.obs mrt.iv.pred
## 1        FALSE  FALSE   FALSE   FALSE    FALSE      FALSE       FALSE
## 2        FALSE  FALSE   FALSE   FALSE    FALSE      FALSE       FALSE
##   mrt.md.obs mrt.md.pred vz.obs vz.pred vss.obs vss.pred vss.iv.obs vss.iv.pred
## 1      FALSE       FALSE  FALSE   FALSE   FALSE    FALSE      FALSE       FALSE
## 2      FALSE       FALSE  FALSE   FALSE   FALSE    FALSE      FALSE       FALSE
##   vss.md.obs vss.md.pred vd.obs vd.pred thalf.eff.obs thalf.eff.pred
## 1      FALSE       FALSE  FALSE   FALSE         FALSE          FALSE
## 2      FALSE       FALSE  FALSE   FALSE         FALSE          FALSE
##   thalf.eff.iv.obs thalf.eff.iv.pred kel.obs kel.pred kel.iv.obs kel.iv.pred
## 1            FALSE             FALSE   FALSE    FALSE      FALSE       FALSE
## 2            FALSE             FALSE   FALSE    FALSE      FALSE       FALSE

After the data is ready, the calculations and summary can progress.

results_obj <- pk.nca(data_obj)
print(results_obj)

## $result
##    start end Subject PPTESTCD    PPORRES exclude
## 1      0  24       1  auclast 151.189916    <NA>
## 2      0  24       1     cmax  10.500000    <NA>
## 3    144 168       1  auclast 214.860975    <NA>
## 4    144 168       1     cmax  15.096727    <NA>
## 5      0  24       2  auclast  88.457261    <NA>
## 6      0  24       2     cmax   8.330000    <NA>
## 7    144 168       2  auclast  97.381127    <NA>
## 8    144 168       2     cmax   9.221742    <NA>
## 9      0  24       3  auclast  95.698098    <NA>
## 10     0  24       3     cmax   8.200000    <NA>
## 11   144 168       3  auclast 106.131322    <NA>
## 12   144 168       3     cmax   9.252474    <NA>
## 13     0  24       4  auclast 101.860775    <NA>
## 14     0  24       4     cmax   8.600000    <NA>
## 15   144 168       4  auclast 114.219220    <NA>
## 16   144 168       4     cmax   9.815170    <NA>
## 17     0  24       5  auclast 117.621805    <NA>
## 18     0  24       5     cmax  11.400000    <NA>
## 19   144 168       5  auclast 136.309916    <NA>
## 20   144 168       5     cmax  13.096191    <NA>
## 21     0  24       6  auclast  71.834110    <NA>
## 22     0  24       6     cmax   6.440000    <NA>
## 23   144 168       6  auclast  82.177601    <NA>
## 24   144 168       6     cmax   7.374375    <NA>
## 25     0  24       7  auclast  89.021716    <NA>
## 26     0  24       7     cmax   7.090000    <NA>
## 27   144 168       7  auclast 100.992404    <NA>
## 28   144 168       7     cmax   8.069847    <NA>
## 29     0  24       8  auclast  86.655906    <NA>
## 30     0  24       8     cmax   7.560000    <NA>
## 31   144 168       8  auclast 102.163105    <NA>
## 32   144 168       8     cmax   8.807391    <NA>
## 33     0  24       9  auclast  83.447367    <NA>
## 34     0  24       9     cmax   9.030000    <NA>
## 35   144 168       9  auclast  97.525881    <NA>
## 36   144 168       9     cmax  10.308349    <NA>
## 37     0  24      10  auclast 136.329998    <NA>
## 38     0  24      10     cmax  10.210000    <NA>
## 39   144 168      10  auclast 167.865640    <NA>
## 40   144 168      10     cmax  12.382817    <NA>
## 41     0  24      11  auclast  77.824409    <NA>
## 42     0  24      11     cmax   8.000000    <NA>
## 43   144 168      11  auclast  86.903095    <NA>
## 44   144 168      11     cmax   8.878024    <NA>
## 45     0  24      12  auclast 115.043218    <NA>
## 46     0  24      12     cmax   9.750000    <NA>
## 47   144 168      12  auclast 125.838198    <NA>
## 48   144 168      12     cmax  10.746939    <NA>
## 
## $data
## Formula for concentration:
##  conc ~ time | Subject
## 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:
##  timeX ~ (time | Subject)
## Nominal time column is not specified.
## 
## First 6 rows of dosing data:
##  Subject time exclude         route duration timeX
##        1    0    <NA> extravascular        0    NA
##        2    0    <NA> extravascular        0    NA
##        3    0    <NA> extravascular        0    NA
##        4    0    <NA> extravascular        0    NA
##        5    0    <NA> extravascular        0    NA
##        6    0    <NA> extravascular        0    NA
## 
## With 2 rows of AUC specifications.
## 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
## 
## $min.hl.points
## [1] 3
## 
## $min.span.ratio
## [1] 2
## 
## $max.aucinf.pext
## [1] 20
## 
## $min.hl.r.squared
## [1] 0.9
## 
## $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 auclast.dn aucall.dn aumclast.dn aumcall.dn  cmax
## 1      FALSE           FALSE      FALSE     FALSE       FALSE      FALSE FALSE
## 2      FALSE           FALSE      FALSE     FALSE       FALSE      FALSE  TRUE
##    cmin  tmax tlast tfirst clast.obs cl.last cl.all     f mrt.last mrt.iv.last
## 1 FALSE FALSE FALSE  FALSE     FALSE   FALSE  FALSE FALSE    FALSE       FALSE
## 2 FALSE  TRUE FALSE  FALSE     FALSE   FALSE  FALSE FALSE    FALSE       FALSE
##   vss.last vss.iv.last   cav ctrough   ptr  tlag deg.fluc swing  ceoi    ae
## 1    FALSE       FALSE FALSE   FALSE FALSE FALSE    FALSE FALSE FALSE FALSE
## 2    FALSE       FALSE FALSE   FALSE FALSE FALSE    FALSE FALSE FALSE FALSE
##   clr.last clr.obs clr.pred    fe half.life r.squared adj.r.squared lambda.z
## 1    FALSE   FALSE    FALSE FALSE     FALSE     FALSE         FALSE    FALSE
## 2    FALSE   FALSE    FALSE FALSE      TRUE     FALSE         FALSE    FALSE
##   lambda.z.time.first lambda.z.n.points clast.pred span.ratio cmax.dn cmin.dn
## 1               FALSE             FALSE      FALSE      FALSE   FALSE   FALSE
## 2               FALSE             FALSE      FALSE      FALSE   FALSE   FALSE
##   clast.obs.dn clast.pred.dn cav.dn ctrough.dn thalf.eff.last thalf.eff.iv.last
## 1        FALSE         FALSE  FALSE      FALSE          FALSE             FALSE
## 2        FALSE         FALSE  FALSE      FALSE          FALSE             FALSE
##   kel.last kel.iv.last aucinf.obs aucinf.pred aumcinf.obs aumcinf.pred
## 1    FALSE       FALSE      FALSE       FALSE       FALSE        FALSE
## 2    FALSE       FALSE       TRUE       FALSE       FALSE        FALSE
##   aucint.inf.obs aucint.inf.obs.dose aucint.inf.pred aucint.inf.pred.dose
## 1          FALSE               FALSE           FALSE                FALSE
## 2          FALSE               FALSE           FALSE                FALSE
##   aucinf.obs.dn aucinf.pred.dn aumcinf.obs.dn aumcinf.pred.dn aucpext.obs
## 1         FALSE          FALSE          FALSE           FALSE       FALSE
## 2         FALSE          FALSE          FALSE           FALSE       FALSE
##   aucpext.pred cl.obs cl.pred mrt.obs mrt.pred mrt.iv.obs mrt.iv.pred
## 1        FALSE  FALSE   FALSE   FALSE    FALSE      FALSE       FALSE
## 2        FALSE  FALSE   FALSE   FALSE    FALSE      FALSE       FALSE
##   mrt.md.obs mrt.md.pred vz.obs vz.pred vss.obs vss.pred vss.iv.obs vss.iv.pred
## 1      FALSE       FALSE  FALSE   FALSE   FALSE    FALSE      FALSE       FALSE
## 2      FALSE       FALSE  FALSE   FALSE   FALSE    FALSE      FALSE       FALSE
##   vss.md.obs vss.md.pred vd.obs vd.pred thalf.eff.obs thalf.eff.pred
## 1      FALSE       FALSE  FALSE   FALSE         FALSE          FALSE
## 2      FALSE       FALSE  FALSE   FALSE         FALSE          FALSE
##   thalf.eff.iv.obs thalf.eff.iv.pred kel.obs kel.pred kel.iv.obs kel.iv.pred
## 1            FALSE             FALSE   FALSE    FALSE      FALSE       FALSE
## 2            FALSE             FALSE   FALSE    FALSE      FALSE       FALSE
## 
## 
## $exclude
## [1] "exclude"
## 
## attr(,"class")
## [1] "PKNCAresults" "list"        
## attr(,"provenance")
## Provenance hash 51b59f882a9f3a2811324a5ff10bb22b generated on 2020-08-10 16:33:01 with R version 3.6.2 (2019-12-12).

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

Computing NCA Parameters for Theophylline

Bill Denney

Load the data

Merge the Concentration and Dose

Compute the parameters

Multiple Dose Example