Computing NCA Parameters for Theophylline

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. Note that Dose in the theophylline dataset is in mg/kg rather than mg; the parameters calculated below do not use the dose amount, but dose-dependent parameters (for example, clearance, volume of distribution, or dose-normalized parameters) would first require converting the dose to mg by multiplying by the subject’s body weight (Wt).

## 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	auclast_se	auclast_df	aucall	aumclast	aumclast_se	aumclast_df	aumcall	aucint.last	aucint.all	aumcint.last	aumcint.all	c0	cmax	cmin	tmax	tmin	tlast	tfirst	clast.obs	cl.last	cl.all	cl.int.all	cl.int.last	mrt.last	mrt.all	mrt.int.all	mrt.int.last	mrt.iv.last	vss.last	vss.iv.last	vss.all	vss.int.all	vss.int.last	cav	cav.int.last	cav.int.all	ctrough	cstart	ptr	tlag	deg.fluc	swing	ceoi	aucabove.predose.all	aucabove.trough.all	count_conc	count_conc_measured	totdose	volpk	ae	clr.last	clr.obs	clr.pred	fe	ertlst	ermax	ertmax	erint	erlst	ratio.cmax	ratio.auclast	ratio.aucint.last	ratio.aucint.all	sparse_auclast	sparse_auc_se	sparse_auc_df	sparse_aumclast	sparse_aumc_se	sparse_aumc_df	time_above	aucivlast	aucivall	aucivint.last	aucivint.all	aucivpbextlast	aucivpbextall	aucivpbextint.last	aucivpbextint.all	aumcivlast	aumcivall	aumcivint.last	aumcivint.all	half.life	r.squared	adj.r.squared	lambda.z.corrxy	lambda.z	lambda.z.time.first	lambda.z.time.last	lambda.z.n.points	clast.pred	span.ratio	tobit_residual	adj_tobit_residual	lambda.z.n.points_blq	thalf.eff.last	thalf.eff.iv.last	kel.last	kel.iv.last	kel.all	kel.int.all	kel.int.last	cl.iv.all	cl.iv.last	cl.ivint.all	cl.ivint.last	cl.sparse.last	f.last	f.int.last	f.int.all	mrt.sparse.last	mrt.iv.all	mrt.ivint.all	mrt.ivint.last	vz.all	vz.int.all	vz.int.last	vz.iv.all	vz.iv.last	vz.ivint.all	vz.ivint.last	vz.last	vss.iv.all	vss.ivint.all	vss.ivint.last	vss.sparse.last	aucinf.obs	aucinf.pred	aumcinf.obs	aumcinf.pred	aucint.inf.obs	aucint.inf.pred	aumcint.inf.obs	aumcint.inf.pred	aucivinf.obs	aucivinf.pred	aucivpbextinf.obs	aucivpbextinf.pred	aumcivinf.obs	aumcivinf.pred	aucpext.obs	aucpext.pred	kel.iv.all	kel.ivint.all	kel.ivint.last	kel.sparse.last	cl.obs	cl.pred	cl.int.inf.obs	cl.int.inf.pred	cl.iv.obs	cl.iv.pred	f.obs	f.pred	f.int.obs	f.int.pred	mrt.obs	mrt.pred	mrt.int.inf.obs	mrt.int.inf.pred	mrt.iv.obs	mrt.iv.pred	mrt.md.obs	mrt.md.pred	mrt.ivmd.obs	mrt.ivmd.pred	vz.obs	vz.pred	vz.int.inf.obs	vz.int.inf.pred	vz.iv.obs	vz.iv.pred	vz.sparse.last	vss.obs	vss.pred	vss.iv.obs	vss.iv.pred	vss.md.obs	vss.md.pred	vss.ivmd.obs	vss.ivmd.pred	vss.int.inf.obs	vss.int.inf.pred	cav.int.inf.obs	cav.int.inf.pred	ratio.aucinf.obs	ratio.aucinf.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	kel.int.inf.obs	kel.int.inf.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	clr.last.dn	clr.obs.dn	clr.pred.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	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	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	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	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	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

knitr::kable(data_obj_automatic$intervals)

end	auclast	auclast_se	auclast_df	aucall	aumclast	aumclast_se	aumclast_df	aumcall	aucint.last	aucint.all	aumcint.last	aumcint.all	c0	cmax	cmin	tmax	tmin	tlast	tfirst	clast.obs	cl.last	cl.all	cl.int.all	cl.int.last	mrt.last	mrt.all	mrt.int.all	mrt.int.last	mrt.iv.last	vss.last	vss.iv.last	vss.all	vss.int.all	vss.int.last	cav	cav.int.last	cav.int.all	ctrough	cstart	ptr	tlag	deg.fluc	swing	ceoi	aucabove.predose.all	aucabove.trough.all	count_conc	count_conc_measured	totdose	volpk	ae	clr.last	clr.obs	clr.pred	fe	ertlst	ermax	ertmax	erint	erlst	ratio.cmax	ratio.auclast	ratio.aucint.last	ratio.aucint.all	sparse_auclast	sparse_auc_se	sparse_auc_df	sparse_aumclast	sparse_aumc_se	sparse_aumc_df	time_above	aucivlast	aucivall	aucivint.last	aucivint.all	aucivpbextlast	aucivpbextall	aucivpbextint.last	aucivpbextint.all	aumcivlast	aumcivall	aumcivint.last	aumcivint.all	half.life	r.squared	adj.r.squared	lambda.z.corrxy	lambda.z	lambda.z.time.first	lambda.z.time.last	lambda.z.n.points	clast.pred	span.ratio	tobit_residual	adj_tobit_residual	lambda.z.n.points_blq	thalf.eff.last	thalf.eff.iv.last	kel.last	kel.iv.last	kel.all	kel.int.all	kel.int.last	cl.iv.all	cl.iv.last	cl.ivint.all	cl.ivint.last	cl.sparse.last	f.last	f.int.last	f.int.all	mrt.sparse.last	mrt.iv.all	mrt.ivint.all	mrt.ivint.last	vz.all	vz.int.all	vz.int.last	vz.iv.all	vz.iv.last	vz.ivint.all	vz.ivint.last	vz.last	vss.iv.all	vss.ivint.all	vss.ivint.last	vss.sparse.last	aucinf.obs	aucinf.pred	aumcinf.obs	aumcinf.pred	aucint.inf.obs	aucint.inf.pred	aumcint.inf.obs	aumcint.inf.pred	aucivinf.obs	aucivinf.pred	aucivpbextinf.obs	aucivpbextinf.pred	aumcivinf.obs	aumcivinf.pred	aucpext.obs	aucpext.pred	kel.iv.all	kel.ivint.all	kel.ivint.last	kel.sparse.last	cl.obs	cl.pred	cl.int.inf.obs	cl.int.inf.pred	cl.iv.obs	cl.iv.pred	f.obs	f.pred	f.int.obs	f.int.pred	mrt.obs	mrt.pred	mrt.int.inf.obs	mrt.int.inf.pred	mrt.iv.obs	mrt.iv.pred	mrt.md.obs	mrt.md.pred	mrt.ivmd.obs	mrt.ivmd.pred	vz.obs	vz.pred	vz.int.inf.obs	vz.int.inf.pred	vz.iv.obs	vz.iv.pred	vz.sparse.last	vss.obs	vss.pred	vss.iv.obs	vss.iv.pred	vss.md.obs	vss.md.pred	vss.ivmd.obs	vss.ivmd.pred	vss.int.inf.obs	vss.int.inf.pred	cav.int.inf.obs	cav.int.inf.pred	ratio.aucinf.obs	ratio.aucinf.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	kel.int.inf.obs	kel.int.inf.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	clr.last.dn	clr.obs.dn	clr.pred.dn	Subject	impute
Inf	TRUE	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	FALSE	FALSE	TRUE	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	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	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	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	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	1	start_predose_conc0
Inf	TRUE	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	FALSE	FALSE	TRUE	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	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	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	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	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	2	start_predose_conc0
Inf	TRUE	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	FALSE	FALSE	TRUE	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	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	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	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	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	3	start_predose_conc0
Inf	TRUE	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	FALSE	FALSE	TRUE	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	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	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	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	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	4	start_predose_conc0
Inf	TRUE	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	FALSE	FALSE	TRUE	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	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	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	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	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	5	start_predose_conc0
Inf	TRUE	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	FALSE	FALSE	TRUE	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	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	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	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	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	6	start_predose_conc0
Inf	TRUE	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	FALSE	FALSE	TRUE	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	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	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	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	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	7	start_predose_conc0
Inf	TRUE	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	FALSE	FALSE	TRUE	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	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	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	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	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	8	start_predose_conc0
Inf	TRUE	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	FALSE	FALSE	TRUE	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	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	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	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	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	9	start_predose_conc0
Inf	TRUE	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	FALSE	FALSE	TRUE	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	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	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	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	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	10	start_predose_conc0
Inf	TRUE	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	FALSE	FALSE	TRUE	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	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	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	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	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	11	start_predose_conc0
Inf	TRUE	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	FALSE	FALSE	TRUE	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	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	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	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	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	12	start_predose_conc0

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- $\infty$}, 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	auclast_se	auclast_df	aucall	aumclast	aumclast_se	aumclast_df	aumcall	aucint.last	aucint.all	aumcint.last	aumcint.all	c0	cmax	cmin	tmax	tmin	tlast	tfirst	clast.obs	cl.last	cl.all	cl.int.all	cl.int.last	mrt.last	mrt.all	mrt.int.all	mrt.int.last	mrt.iv.last	vss.last	vss.iv.last	vss.all	vss.int.all	vss.int.last	cav	cav.int.last	cav.int.all	ctrough	cstart	ptr	tlag	deg.fluc	swing	ceoi	aucabove.predose.all	aucabove.trough.all	count_conc	count_conc_measured	totdose	volpk	ae	clr.last	clr.obs	clr.pred	fe	ertlst	ermax	ertmax	erint	erlst	ratio.cmax	ratio.auclast	ratio.aucint.last	ratio.aucint.all	sparse_auclast	sparse_auc_se	sparse_auc_df	sparse_aumclast	sparse_aumc_se	sparse_aumc_df	time_above	aucivlast	aucivall	aucivint.last	aucivint.all	aucivpbextlast	aucivpbextall	aucivpbextint.last	aucivpbextint.all	aumcivlast	aumcivall	aumcivint.last	aumcivint.all	half.life	r.squared	adj.r.squared	lambda.z.corrxy	lambda.z	lambda.z.time.first	lambda.z.time.last	lambda.z.n.points	clast.pred	span.ratio	tobit_residual	adj_tobit_residual	lambda.z.n.points_blq	thalf.eff.last	thalf.eff.iv.last	kel.last	kel.iv.last	kel.all	kel.int.all	kel.int.last	cl.iv.all	cl.iv.last	cl.ivint.all	cl.ivint.last	cl.sparse.last	f.last	f.int.last	f.int.all	mrt.sparse.last	mrt.iv.all	mrt.ivint.all	mrt.ivint.last	vz.all	vz.int.all	vz.int.last	vz.iv.all	vz.iv.last	vz.ivint.all	vz.ivint.last	vz.last	vss.iv.all	vss.ivint.all	vss.ivint.last	vss.sparse.last	aucinf.obs	aucinf.pred	aumcinf.obs	aumcinf.pred	aucint.inf.obs	aucint.inf.pred	aumcint.inf.obs	aumcint.inf.pred	aucivinf.obs	aucivinf.pred	aucivpbextinf.obs	aucivpbextinf.pred	aumcivinf.obs	aumcivinf.pred	aucpext.obs	aucpext.pred	kel.iv.all	kel.ivint.all	kel.ivint.last	kel.sparse.last	cl.obs	cl.pred	cl.int.inf.obs	cl.int.inf.pred	cl.iv.obs	cl.iv.pred	f.obs	f.pred	f.int.obs	f.int.pred	mrt.obs	mrt.pred	mrt.int.inf.obs	mrt.int.inf.pred	mrt.iv.obs	mrt.iv.pred	mrt.md.obs	mrt.md.pred	mrt.ivmd.obs	mrt.ivmd.pred	vz.obs	vz.pred	vz.int.inf.obs	vz.int.inf.pred	vz.iv.obs	vz.iv.pred	vz.sparse.last	vss.obs	vss.pred	vss.iv.obs	vss.iv.pred	vss.md.obs	vss.md.pred	vss.ivmd.obs	vss.ivmd.pred	vss.int.inf.obs	vss.int.inf.pred	cav.int.inf.obs	cav.int.inf.pred	ratio.aucinf.obs	ratio.aucinf.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	kel.int.inf.obs	kel.int.inf.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	clr.last.dn	clr.obs.dn	clr.pred.dn
0	Inf	TRUE	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	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	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	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	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	end	PPTESTCD	PPORRES	PPANMETH	exclude
1	Inf	auclast	147.2347	Imputation: start_predose_conc0. AUC: lin up/log down	NA
1	Inf	cmax	10.5000	Imputation: start_predose_conc0	NA
1	Inf	tmax	1.1200	Imputation: start_predose_conc0	NA
1	Inf	tlast	24.3700	Imputation: start_predose_conc0	NA
1	Inf	clast.obs	3.2800	Imputation: start_predose_conc0	NA
1	Inf	tlag	0.0000	Imputation: start_predose_conc0	NA

summary(results_obj_automatic)

start	end	N	auclast	cmax	tmax	tlag	count_conc	half.life	aucinf.obs	aucpext.obs	cl.obs
0	Inf	12	98.7 [22.5]	8.65 [17.0]	1.14 [0.630, 3.55]	0.000 [0.000, 0.000]	11.0 [11.0, 11.0]	8.18 [2.12]	115 [28.4]	13.8 [6.34]	0.0398 [29.4]

results_obj_manual <- pk.nca(data_obj_manual)
knitr::kable(head(as.data.frame(results_obj_manual)))

Subject	end	PPTESTCD	PPORRES	PPANMETH	exclude
6	Inf	auclast	71.6970150	AUC: lin up/log down	NA
6	Inf	cmax	6.4400000		NA
6	Inf	tmax	1.1500000		NA
6	Inf	tlast	23.8500000		NA
6	Inf	clast.obs	0.9200000		NA
6	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).

conc_obj <- PKNCAconc(as.data.frame(datasets::Theoph), conc~Time|Subject)
conc_obj_multi <-
  PKNCAconc(
    superposition(conc_obj,
                  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
##        1  0.74000 0.00    <NA>
##        1  2.84000 0.25    <NA>
##        1  4.23875 0.37    <NA>
##        1  6.57000 0.57    <NA>
##        1 10.50000 1.12    <NA>
##        1  9.66000 2.02    <NA>

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 auclast_se auclast_df aucall aumclast aumclast_se
## 1     0  24    TRUE      FALSE      FALSE  FALSE    FALSE       FALSE
## 2   144 168    TRUE      FALSE      FALSE  FALSE    FALSE       FALSE
##   aumclast_df aumcall aucint.last aucint.all aumcint.last aumcint.all    c0
## 1       FALSE   FALSE       FALSE      FALSE        FALSE       FALSE FALSE
## 2       FALSE   FALSE       FALSE      FALSE        FALSE       FALSE FALSE
##   cmax  cmin  tmax  tmin tlast tfirst clast.obs cl.last cl.all cl.int.all
## 1 TRUE FALSE FALSE FALSE FALSE  FALSE     FALSE   FALSE  FALSE      FALSE
## 2 TRUE FALSE FALSE FALSE FALSE  FALSE     FALSE   FALSE  FALSE      FALSE
##   cl.int.last mrt.last mrt.all mrt.int.all mrt.int.last mrt.iv.last vss.last
## 1       FALSE    FALSE   FALSE       FALSE        FALSE       FALSE    FALSE
## 2       FALSE    FALSE   FALSE       FALSE        FALSE       FALSE    FALSE
##   vss.iv.last vss.all vss.int.all vss.int.last   cav cav.int.last cav.int.all
## 1       FALSE   FALSE       FALSE        FALSE FALSE        FALSE       FALSE
## 2       FALSE   FALSE       FALSE        FALSE FALSE        FALSE       FALSE
##   ctrough cstart   ptr  tlag deg.fluc swing  ceoi aucabove.predose.all
## 1   FALSE  FALSE FALSE FALSE    FALSE FALSE FALSE                FALSE
## 2   FALSE  FALSE FALSE FALSE    FALSE FALSE FALSE                FALSE
##   aucabove.trough.all count_conc count_conc_measured totdose volpk    ae
## 1               FALSE      FALSE               FALSE   FALSE FALSE FALSE
## 2               FALSE      FALSE               FALSE   FALSE FALSE FALSE
##   clr.last clr.obs clr.pred    fe ertlst ermax ertmax erint erlst ratio.cmax
## 1    FALSE   FALSE    FALSE FALSE  FALSE FALSE  FALSE FALSE FALSE      FALSE
## 2    FALSE   FALSE    FALSE FALSE  FALSE FALSE  FALSE FALSE FALSE      FALSE
##   ratio.auclast ratio.aucint.last ratio.aucint.all sparse_auclast sparse_auc_se
## 1         FALSE             FALSE            FALSE          FALSE         FALSE
## 2         FALSE             FALSE            FALSE          FALSE         FALSE
##   sparse_auc_df sparse_aumclast sparse_aumc_se sparse_aumc_df time_above
## 1         FALSE           FALSE          FALSE          FALSE      FALSE
## 2         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 aumcivlast aumcivall aumcivint.last
## 1              FALSE             FALSE      FALSE     FALSE          FALSE
## 2              FALSE             FALSE      FALSE     FALSE          FALSE
##   aumcivint.all half.life r.squared adj.r.squared lambda.z.corrxy lambda.z
## 1         FALSE     FALSE     FALSE         FALSE           FALSE    FALSE
## 2         FALSE     FALSE     FALSE         FALSE           FALSE    FALSE
##   lambda.z.time.first lambda.z.time.last lambda.z.n.points clast.pred
## 1               FALSE              FALSE             FALSE      FALSE
## 2               FALSE              FALSE             FALSE      FALSE
##   span.ratio tobit_residual adj_tobit_residual lambda.z.n.points_blq
## 1      FALSE          FALSE              FALSE                 FALSE
## 2      FALSE          FALSE              FALSE                 FALSE
##   thalf.eff.last thalf.eff.iv.last kel.last kel.iv.last kel.all kel.int.all
## 1          FALSE             FALSE    FALSE       FALSE   FALSE       FALSE
## 2          FALSE             FALSE    FALSE       FALSE   FALSE       FALSE
##   kel.int.last cl.iv.all cl.iv.last cl.ivint.all cl.ivint.last cl.sparse.last
## 1        FALSE     FALSE      FALSE        FALSE         FALSE          FALSE
## 2        FALSE     FALSE      FALSE        FALSE         FALSE          FALSE
##   f.last f.int.last f.int.all mrt.sparse.last mrt.iv.all mrt.ivint.all
## 1  FALSE      FALSE     FALSE           FALSE      FALSE         FALSE
## 2  FALSE      FALSE     FALSE           FALSE      FALSE         FALSE
##   mrt.ivint.last vz.all vz.int.all vz.int.last vz.iv.all vz.iv.last
## 1          FALSE  FALSE      FALSE       FALSE     FALSE      FALSE
## 2          FALSE  FALSE      FALSE       FALSE     FALSE      FALSE
##   vz.ivint.all vz.ivint.last vz.last vss.iv.all vss.ivint.all vss.ivint.last
## 1        FALSE         FALSE   FALSE      FALSE         FALSE          FALSE
## 2        FALSE         FALSE   FALSE      FALSE         FALSE          FALSE
##   vss.sparse.last aucinf.obs aucinf.pred aumcinf.obs aumcinf.pred
## 1           FALSE      FALSE       FALSE       FALSE        FALSE
## 2           FALSE      FALSE       FALSE       FALSE        FALSE
##   aucint.inf.obs aucint.inf.pred aumcint.inf.obs aumcint.inf.pred aucivinf.obs
## 1          FALSE           FALSE           FALSE            FALSE        FALSE
## 2          FALSE           FALSE           FALSE            FALSE        FALSE
##   aucivinf.pred aucivpbextinf.obs aucivpbextinf.pred aumcivinf.obs
## 1         FALSE             FALSE              FALSE         FALSE
## 2         FALSE             FALSE              FALSE         FALSE
##   aumcivinf.pred aucpext.obs aucpext.pred kel.iv.all kel.ivint.all
## 1          FALSE       FALSE        FALSE      FALSE         FALSE
## 2          FALSE       FALSE        FALSE      FALSE         FALSE
##   kel.ivint.last kel.sparse.last cl.obs cl.pred cl.int.inf.obs cl.int.inf.pred
## 1          FALSE           FALSE  FALSE   FALSE          FALSE           FALSE
## 2          FALSE           FALSE  FALSE   FALSE          FALSE           FALSE
##   cl.iv.obs cl.iv.pred f.obs f.pred f.int.obs f.int.pred mrt.obs mrt.pred
## 1     FALSE      FALSE FALSE  FALSE     FALSE      FALSE   FALSE    FALSE
## 2     FALSE      FALSE FALSE  FALSE     FALSE      FALSE   FALSE    FALSE
##   mrt.int.inf.obs mrt.int.inf.pred mrt.iv.obs mrt.iv.pred mrt.md.obs
## 1           FALSE            FALSE      FALSE       FALSE      FALSE
## 2           FALSE            FALSE      FALSE       FALSE      FALSE
##   mrt.md.pred mrt.ivmd.obs mrt.ivmd.pred vz.obs vz.pred vz.int.inf.obs
## 1       FALSE        FALSE         FALSE  FALSE   FALSE          FALSE
## 2       FALSE        FALSE         FALSE  FALSE   FALSE          FALSE
##   vz.int.inf.pred vz.iv.obs vz.iv.pred vz.sparse.last vss.obs vss.pred
## 1           FALSE     FALSE      FALSE          FALSE   FALSE    FALSE
## 2           FALSE     FALSE      FALSE          FALSE   FALSE    FALSE
##   vss.iv.obs vss.iv.pred vss.md.obs vss.md.pred vss.ivmd.obs vss.ivmd.pred
## 1      FALSE       FALSE      FALSE       FALSE        FALSE         FALSE
## 2      FALSE       FALSE      FALSE       FALSE        FALSE         FALSE
##   vss.int.inf.obs vss.int.inf.pred cav.int.inf.obs cav.int.inf.pred
## 1           FALSE            FALSE           FALSE            FALSE
## 2           FALSE            FALSE           FALSE            FALSE
##   ratio.aucinf.obs ratio.aucinf.pred thalf.eff.obs thalf.eff.pred
## 1            FALSE             FALSE         FALSE          FALSE
## 2            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
##   kel.int.inf.obs kel.int.inf.pred auclast.dn aucall.dn aucinf.obs.dn
## 1           FALSE            FALSE      FALSE     FALSE         FALSE
## 2           FALSE            FALSE      FALSE     FALSE         FALSE
##   aucinf.pred.dn aumclast.dn aumcall.dn aumcinf.obs.dn aumcinf.pred.dn cmax.dn
## 1          FALSE       FALSE      FALSE          FALSE           FALSE   FALSE
## 2          FALSE       FALSE      FALSE          FALSE           FALSE   FALSE
##   cmin.dn clast.obs.dn clast.pred.dn cav.dn ctrough.dn clr.last.dn clr.obs.dn
## 1   FALSE        FALSE         FALSE  FALSE      FALSE       FALSE      FALSE
## 2   FALSE        FALSE         FALSE  FALSE      FALSE       FALSE      FALSE
##   clr.pred.dn
## 1       FALSE
## 2       FALSE

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

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

## $result
## # A tibble: 48 × 7
##    Subject start   end PPTESTCD PPORRES PPANMETH               exclude
##    <ord>   <dbl> <dbl> <chr>      <dbl> <chr>                  <chr>  
##  1 6           0    24 auclast    71.8  "AUC: lin up/log down" NA     
##  2 6           0    24 cmax        6.44 ""                     NA     
##  3 6         144   168 auclast    82.2  "AUC: lin up/log down" NA     
##  4 6         144   168 cmax        7.37 ""                     NA     
##  5 7           0    24 auclast    89.0  "AUC: lin up/log down" NA     
##  6 7           0    24 cmax        7.09 ""                     NA     
##  7 7         144   168 auclast   101.   "AUC: lin up/log down" NA     
##  8 7         144   168 cmax        8.07 ""                     NA     
##  9 8           0    24 auclast    86.7  "AUC: lin up/log down" 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
##        1  0.74000 0.00    <NA>
##        1  2.84000 0.25    <NA>
##        1  4.23875 0.37    <NA>
##        1  6.57000 0.57    <NA>
##        1 10.50000 1.12    <NA>
##        1  9.66000 2.02    <NA>
## 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.
## Options changed from default are:
## $adj.r.squared.factor
## [1] 1e-04
## 
## $r.squared.factor
## [1] NA
## 
## $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"
## 
## 
## $debug
## NULL
## 
## $first.tmax
## [1] TRUE
## 
## $first.tmin
## [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
## 
## $auto.interval.method
## [1] "builder"
## 
## $auto.interval.tolerance
## [1] 0.05
## 
## $single.dose.aucs
##   start end auclast auclast_se auclast_df aucall aumclast aumclast_se
## 1     0  24    TRUE      FALSE      FALSE  FALSE    FALSE       FALSE
## 2     0 Inf   FALSE      FALSE      FALSE  FALSE    FALSE       FALSE
##   aumclast_df aumcall aucint.last aucint.all aumcint.last aumcint.all    c0
## 1       FALSE   FALSE       FALSE      FALSE        FALSE       FALSE FALSE
## 2       FALSE   FALSE       FALSE      FALSE        FALSE       FALSE FALSE
##    cmax  cmin  tmax  tmin tlast tfirst clast.obs cl.last cl.all cl.int.all
## 1 FALSE FALSE FALSE FALSE FALSE  FALSE     FALSE   FALSE  FALSE      FALSE
## 2  TRUE FALSE  TRUE FALSE FALSE  FALSE     FALSE   FALSE  FALSE      FALSE
##   cl.int.last mrt.last mrt.all mrt.int.all mrt.int.last mrt.iv.last vss.last
## 1       FALSE    FALSE   FALSE       FALSE        FALSE       FALSE    FALSE
## 2       FALSE    FALSE   FALSE       FALSE        FALSE       FALSE    FALSE
##   vss.iv.last vss.all vss.int.all vss.int.last   cav cav.int.last cav.int.all
## 1       FALSE   FALSE       FALSE        FALSE FALSE        FALSE       FALSE
## 2       FALSE   FALSE       FALSE        FALSE FALSE        FALSE       FALSE
##   ctrough cstart   ptr  tlag deg.fluc swing  ceoi aucabove.predose.all
## 1   FALSE  FALSE FALSE FALSE    FALSE FALSE FALSE                FALSE
## 2   FALSE  FALSE FALSE FALSE    FALSE FALSE FALSE                FALSE
##   aucabove.trough.all count_conc count_conc_measured totdose volpk    ae
## 1               FALSE      FALSE               FALSE   FALSE FALSE FALSE
## 2               FALSE      FALSE               FALSE   FALSE FALSE FALSE
##   clr.last clr.obs clr.pred    fe ertlst ermax ertmax erint erlst ratio.cmax
## 1    FALSE   FALSE    FALSE FALSE  FALSE FALSE  FALSE FALSE FALSE      FALSE
## 2    FALSE   FALSE    FALSE FALSE  FALSE FALSE  FALSE FALSE FALSE      FALSE
##   ratio.auclast ratio.aucint.last ratio.aucint.all sparse_auclast sparse_auc_se
## 1         FALSE             FALSE            FALSE          FALSE         FALSE
## 2         FALSE             FALSE            FALSE          FALSE         FALSE
##   sparse_auc_df sparse_aumclast sparse_aumc_se sparse_aumc_df time_above
## 1         FALSE           FALSE          FALSE          FALSE      FALSE
## 2         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 aumcivlast aumcivall aumcivint.last
## 1              FALSE             FALSE      FALSE     FALSE          FALSE
## 2              FALSE             FALSE      FALSE     FALSE          FALSE
##   aumcivint.all half.life r.squared adj.r.squared lambda.z.corrxy lambda.z
## 1         FALSE     FALSE     FALSE         FALSE           FALSE    FALSE
## 2         FALSE      TRUE     FALSE         FALSE           FALSE    FALSE
##   lambda.z.time.first lambda.z.time.last lambda.z.n.points clast.pred
## 1               FALSE              FALSE             FALSE      FALSE
## 2               FALSE              FALSE             FALSE      FALSE
##   span.ratio tobit_residual adj_tobit_residual lambda.z.n.points_blq
## 1      FALSE          FALSE              FALSE                 FALSE
## 2      FALSE          FALSE              FALSE                 FALSE
##   thalf.eff.last thalf.eff.iv.last kel.last kel.iv.last kel.all kel.int.all
## 1          FALSE             FALSE    FALSE       FALSE   FALSE       FALSE
## 2          FALSE             FALSE    FALSE       FALSE   FALSE       FALSE
##   kel.int.last cl.iv.all cl.iv.last cl.ivint.all cl.ivint.last cl.sparse.last
## 1        FALSE     FALSE      FALSE        FALSE         FALSE          FALSE
## 2        FALSE     FALSE      FALSE        FALSE         FALSE          FALSE
##   f.last f.int.last f.int.all mrt.sparse.last mrt.iv.all mrt.ivint.all
## 1  FALSE      FALSE     FALSE           FALSE      FALSE         FALSE
## 2  FALSE      FALSE     FALSE           FALSE      FALSE         FALSE
##   mrt.ivint.last vz.all vz.int.all vz.int.last vz.iv.all vz.iv.last
## 1          FALSE  FALSE      FALSE       FALSE     FALSE      FALSE
## 2          FALSE  FALSE      FALSE       FALSE     FALSE      FALSE
##   vz.ivint.all vz.ivint.last vz.last vss.iv.all vss.ivint.all vss.ivint.last
## 1        FALSE         FALSE   FALSE      FALSE         FALSE          FALSE
## 2        FALSE         FALSE   FALSE      FALSE         FALSE          FALSE
##   vss.sparse.last aucinf.obs aucinf.pred aumcinf.obs aumcinf.pred
## 1           FALSE      FALSE       FALSE       FALSE        FALSE
## 2           FALSE       TRUE       FALSE       FALSE        FALSE
##   aucint.inf.obs aucint.inf.pred aumcint.inf.obs aumcint.inf.pred aucivinf.obs
## 1          FALSE           FALSE           FALSE            FALSE        FALSE
## 2          FALSE           FALSE           FALSE            FALSE        FALSE
##   aucivinf.pred aucivpbextinf.obs aucivpbextinf.pred aumcivinf.obs
## 1         FALSE             FALSE              FALSE         FALSE
## 2         FALSE             FALSE              FALSE         FALSE
##   aumcivinf.pred aucpext.obs aucpext.pred kel.iv.all kel.ivint.all
## 1          FALSE       FALSE        FALSE      FALSE         FALSE
## 2          FALSE       FALSE        FALSE      FALSE         FALSE
##   kel.ivint.last kel.sparse.last cl.obs cl.pred cl.int.inf.obs cl.int.inf.pred
## 1          FALSE           FALSE  FALSE   FALSE          FALSE           FALSE
## 2          FALSE           FALSE  FALSE   FALSE          FALSE           FALSE
##   cl.iv.obs cl.iv.pred f.obs f.pred f.int.obs f.int.pred mrt.obs mrt.pred
## 1     FALSE      FALSE FALSE  FALSE     FALSE      FALSE   FALSE    FALSE
## 2     FALSE      FALSE FALSE  FALSE     FALSE      FALSE   FALSE    FALSE
##   mrt.int.inf.obs mrt.int.inf.pred mrt.iv.obs mrt.iv.pred mrt.md.obs
## 1           FALSE            FALSE      FALSE       FALSE      FALSE
## 2           FALSE            FALSE      FALSE       FALSE      FALSE
##   mrt.md.pred mrt.ivmd.obs mrt.ivmd.pred vz.obs vz.pred vz.int.inf.obs
## 1       FALSE        FALSE         FALSE  FALSE   FALSE          FALSE
## 2       FALSE        FALSE         FALSE  FALSE   FALSE          FALSE
##   vz.int.inf.pred vz.iv.obs vz.iv.pred vz.sparse.last vss.obs vss.pred
## 1           FALSE     FALSE      FALSE          FALSE   FALSE    FALSE
## 2           FALSE     FALSE      FALSE          FALSE   FALSE    FALSE
##   vss.iv.obs vss.iv.pred vss.md.obs vss.md.pred vss.ivmd.obs vss.ivmd.pred
## 1      FALSE       FALSE      FALSE       FALSE        FALSE         FALSE
## 2      FALSE       FALSE      FALSE       FALSE        FALSE         FALSE
##   vss.int.inf.obs vss.int.inf.pred cav.int.inf.obs cav.int.inf.pred
## 1           FALSE            FALSE           FALSE            FALSE
## 2           FALSE            FALSE           FALSE            FALSE
##   ratio.aucinf.obs ratio.aucinf.pred thalf.eff.obs thalf.eff.pred
## 1            FALSE             FALSE         FALSE          FALSE
## 2            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
##   kel.int.inf.obs kel.int.inf.pred auclast.dn aucall.dn aucinf.obs.dn
## 1           FALSE            FALSE      FALSE     FALSE         FALSE
## 2           FALSE            FALSE      FALSE     FALSE         FALSE
##   aucinf.pred.dn aumclast.dn aumcall.dn aumcinf.obs.dn aumcinf.pred.dn cmax.dn
## 1          FALSE       FALSE      FALSE          FALSE           FALSE   FALSE
## 2          FALSE       FALSE      FALSE          FALSE           FALSE   FALSE
##   cmin.dn clast.obs.dn clast.pred.dn cav.dn ctrough.dn clr.last.dn clr.obs.dn
## 1   FALSE        FALSE         FALSE  FALSE      FALSE       FALSE      FALSE
## 2   FALSE        FALSE         FALSE  FALSE      FALSE       FALSE      FALSE
##   clr.pred.dn
## 1       FALSE
## 2       FALSE
## 
## $allow_partial_missing_units
## [1] FALSE
## 
## $hl_method
## [1] "log-linear"
## 
## $tobit_n_points_penalty
## [1] 0
## 
## $tobit_optim_control
## list()
## 
## 
## $columns
## $columns$exclude
## [1] "exclude"
## 
## 
## attr(,"class")
## [1] "PKNCAresults" "list"        
## attr(,"provenance")
## Provenance hash 1010dd656fa2060bf5d457ba131dbe6d generated on 2026-10-02 01:38:18.084875 with R version 4.6.1 (2026-06-24).

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

Bill Denney

Load the data

Merge the Concentration and Dose

Compute the parameters

Multiple Dose Example