Options for Controlling PKNCA

Summary

PKNCA has many options that control its function. These options have effects throughout the package. The options are controlled using either the PKNCA.options function or by passing the options argument to any of the functions with that as an argument. All options supported by the current version of PKNCA (0.12.1.9002) are listed below with their descriptions.

Options

adj.r.squared.factor

During the calculation of lambda.z, all candidate regressions with an adjusted r^2 within adj.r.squared.factor of the best adjusted r^2 are considered acceptable, and the acceptable regression using the most data points is selected. It allows for more data points to be preferred in the calculation of half-life. Setting either of adj.r.squared.factor and r.squared.factor takes the other out of use, so exactly one of them is ever in use; setting this one to NA therefore selects on the other r-squared.

The default value is: 1e-04

r.squared.factor

During the calculation of lambda.z, all candidate regressions with an r^2 within r.squared.factor of the best r^2 are considered acceptable, and the acceptable regression using the most data points is selected. Unlike the adjusted r^2, the r^2 does not reward more data points, so it generally selects fewer of them. Setting either of adj.r.squared.factor and r.squared.factor takes the other out of use, so exactly one of them is ever in use; setting this one to NA therefore selects on the other r-squared.

The default value is: NA

max.missing

The maximum fraction of the data that may be missing (‘NA’) to calculate summary statistics with the business.* functions.

The default value is: 0.5

auc.method

The method used to calculate the AUC and related statistics. Options are: “lin up/log down”, “linear”, “lin-log”

The default value is: lin up/log down

conc.na

How should missing (‘NA’) concentration values be handled? See help for ‘clean.conc.na’ for how to use this option.

The default value is: drop

conc.blq

How should below the limit of quantification (zero, 0) concentration values be handled? See help for ‘clean.conc.blq’ for how to use this option.

$first [1] “keep”

$middle [1] “drop”

$last [1] “keep”

debug

Enable PKNCA debugging mode (not for production use)

NULL

first.tmax

If there is more than one time point with the maximum value (Cmax or ERmax), which time should be selected for Tmax/ERTmax? If ‘TRUE’, the first will be selected. If ‘FALSE’, the last will be selected.

The default value is: TRUE

first.tmin

If there is more than one time point with the minimum value (Cmin), which time should be selected for Tmin? If ‘TRUE’, the first will be selected. If ‘FALSE’, the last will be selected.

The default value is: TRUE

allow.tmax.in.half.life

Should the concentration and time at Tmax be allowed in the half-life calculation? ‘TRUE’ is yes and ‘FALSE’ is no.

The default value is: FALSE

keep_interval_cols

What additional columns from the intervals should be kept in the results?

NULL

min.hl.points

What is the minimum number of points required to calculate half-life?

The default value is: 3

max.hl.points

What is the maximum number of points that automatic point selection may use for half-life? It must be more than min.hl.points.

The default value is: Inf

min.hl.start.time

What is the earliest time at which automatic point selection may start the half-life (0 does not restrict it)?

The default value is: 0

min.span.ratio

What is the minimum span ratio required to consider a half-life valid?

The default value is: 2

max.aucinf.pext

What is the maximum percent extrapolation to consider an AUCinf valid?

The default value is: 20

min.hl.r.squared

What is the minimum r-squared value to consider a half-life calculation valid?

The default value is: 0.9

progress

A value to pass to purrr::pmap(.progress = ) to create a progress bar while running

The default value is: TRUE

tau.choices

What values for tau (repeating interdose interval) should be considered when attempting to automatically determine the intervals for multiple dosing? See ‘choose.auc.intervals’ and ‘find.tau’ for more information. ‘NA’ means automatically look at any potential interval.

The default value is: NA

auto.interval.method

When automatically determining the intervals, where do the parameters to calculate come from? ‘builder’ asks ‘pknca_interval_table’ for the parameters that suit each interval’s context. ‘legacy’ uses the parameter lists PKNCA used before that was available: the ‘single.dose.aucs’ option for single-dose data, and AUClast, Cmax, and Tmax for each interval of multiple-dose data. Only the choice of parameters differs; the intervals themselves are found the same way either way. See ‘choose.auc.intervals’ for more information.

The default value is: builder

auto.interval.tolerance

When automatically determining the intervals, how far from the boundary of an interval may a sample be drawn and still count as the sample at that boundary? It is given as a fraction of the interval’s length (the dosing interval, tau, for a dosing interval), so that a trough drawn at 167.5 hours still ends an interval nominally ending at 168 hours. See ‘choose.auc.intervals’ for more information.

The default value is: 0.05

single.dose.aucs

When data is single-dose, what intervals should be used?

start end auclast auclast_se auclast_df aucall aucall_se aucall_df aumclast aumclast_se aumclast_df aumcall aumcall_se aumcall_df 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 aucivlast_se aucivlast_df aucivall aucivall_se aucivall_df aucivint.last aucivint.all aucivpbextlast aucivpbextall aucivpbextint.last aucivpbextint.all aumcivlast aumcivlast_se aumcivlast_df aumcivall aumcivall_se aumcivall_df 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.obs_se aucinf.obs_df aucinf.pred aumcinf.obs aumcinf.obs_se aumcinf.obs_df aumcinf.pred aucint.inf.obs aucint.inf.pred aumcint.inf.obs aumcint.inf.pred aucivinf.obs aucivinf.obs_se aucivinf.obs_df aucivinf.pred aucivpbextinf.obs aucivpbextinf.pred aumcivinf.obs aumcivinf.obs_se aumcivinf.obs_df 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
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allow_partial_missing_units

When using unit assignment and conversions, should some units be allowed to be missing?

The default value is: FALSE

hl_method

The method used to calculate the half-life and related parameters. Options are: “log-linear”, “tobit”

The default value is: log-linear

sparse_lambda_z_se

How the standard error of a sparse AUCinf,obs or AUMCinf,obs accounts for the estimated lambda.z: “delta” (the default) adds its uncertainty with the delta method, and “none” treats it as known (Yuan 1993). Options are: “delta”, “none”

The default value is: delta

tobit_n_points_penalty

The penalty exponent applied to the number of points when selecting the best Tobit regression half-life fit. The selection criterion is tobit_residual * n_points ^ tobit_n_points_penalty, and the window minimizing this criterion is selected. A value of 0 (the default) uses the raw Tobit residual with no point-count penalty.

The default value is: 0

tobit_optim_control

A list of control parameters passed to stats::optim() when fitting the Tobit regression half-life. See ?stats::optim for available options.

list()