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Type:	Package
Title:	Perform Pharmacokinetic Non-Compartmental Analysis
Version:	0.12.1
Imports:	checkmate, dplyr (≥ 0.5.0), digest, nlme, purrr, rlang,stats, tidyr, tibble, utils, lifecycle
Suggests:	covr, cowplot, ggplot2, knitr, labeling, pander, pmxTools,rmarkdown, spelling, testthat (≥ 3.0.0), units, withr
Description:	Compute standard Non-Compartmental Analysis (NCA) parameters for typical pharmacokinetic analyses and summarize them.
License:	AGPL-3
URL:	https://humanpred.github.io/pknca/,https://github.com/humanpred/pknca,http://humanpred.github.io/pknca/
BugReports:	https://github.com/humanpred/pknca/issues
NeedsCompilation:	no
VignetteBuilder:	knitr
RoxygenNote:	7.3.2
Config/testthat/edition:	3
Encoding:	UTF-8
Language:	en-US
Packaged:	2025-08-19 00:38:28 UTC; bill
Author:	Bill Denney [aut, cre], Clare Buckeridge [aut], Gerardo Jose Rodriguez [aut], Sridhar Duvvuri [ctb]
Maintainer:	Bill Denney <wdenney@humanpredictions.com>
Repository:	CRAN
Date/Publication:	2025-08-19 04:40:02 UTC

Compute noncompartmental pharmacokinetics

Description

Compute pharmacokinetic (PK) noncompartmental analysis (NCA)parameters.

Details

PKNCA has been cross-validated with both Phoenix WinNonlin(R) and Pumas(click here for thecross-validation article)

A common workflow would load data from a file or database into adata.frame then run the following code.

Author(s)

Maintainer: Bill Denneywdenney@humanpredictions.com (ORCID)

Authors:

• Clare Buckeridgeclare.buckeridge@pfizer.com

• Gerardo Jose Rodriguezgerardo.jrac@gmail.com (ORCID)

Other contributors:

• Sridhar Duvvuri [contributor]

See Also

Useful links:

• https://humanpred.github.io/pknca/

• https://github.com/humanpred/pknca

• http://humanpred.github.io/pknca/

• Report bugs athttps://github.com/humanpred/pknca/issues

Examples

## Not run: # Load concentration-time data into a data.frame called d.conc# with columns named "conc", "time", and "subject".my.conc <- PKNCAconc(d.conc, conc~time|subject)# Load dose-time data into a data.frame called d.dose# with columns named "dose", "time", and "subject".my.dose <- PKNCAdose(d.dose, dose~time|subject)# Combine the concentration-time and dose-time data into an object# ready for calculations.my.data <- PKNCAdata(my.conc, my.dose)# Perform the calculationsmy.results <- pk.nca(my.data)# Look at summary resultssummary(my.results)# Look at a listing of resultsas.data.frame(my.results)## End(Not run)

Choose either the value from an option list or the current set value for anoption.

Description

Choose either the value from an option list or the current set value for anoption.

Usage

PKNCA.choose.option(name, value = NULL, options = list())

Arguments

Value

The value of the option first from theoptions list and if it isnot there then from the current settings.

See Also

Other PKNCA calculation and summary settings:PKNCA.options(),PKNCA.set.summary()

Set default options for PKNCA functions

Description

This function will set the default PKNCA options. If given no inputs, itwill provide the current option set. If given name/value pairs, it will setthe option (as in theoptions() function). If given a name, it will returnthe value for the parameter. If given thedefault option as true, it willprovide the default options.

Usage

PKNCA.options(..., default = FALSE, check = FALSE, name, value)

Arguments

Details

Options are either for calculation or summary functions. Calculation optionsare required for a calculation function to report a result (otherwise thereported value will beNA). Summary options are used during summarizationand are used for assessing what values are included in the summary.

See the vignette 'Options for Controlling PKNCA' for a current list ofoptions (vignette("Options-for-Controlling-PKNCA", package="PKNCA")).

Value

If...

no arguments are given

returns the current options.

a value is set (including the defaults)

returnsNULL

a single value is requested

the current value of that option is returned as a scalar

multiple values are requested

the current values of those options are returned as a list

See Also

PKNCA.options.describe()

Other PKNCA calculation and summary settings:PKNCA.choose.option(),PKNCA.set.summary()

Examples

PKNCA.options()PKNCA.options(default=TRUE)PKNCA.options("auc.method")PKNCA.options(name="auc.method")PKNCA.options(auc.method="lin up/log down", min.hl.points=3)

Describe a PKNCA.options option by name.

Description

Describe a PKNCA.options option by name.

Usage

PKNCA.options.describe(name)

Arguments

Value

A character string of the description.

See Also

PKNCA.options()

Define how NCA parameters are summarized.

Description

Define how NCA parameters are summarized.

Usage

PKNCA.set.summary(  name,  description,  point,  spread,  rounding = list(signif = 3),  reset = FALSE)

Arguments

Value

All current summary settings (invisibly)

See Also

summary.PKNCAresults()

Other PKNCA calculation and summary settings:PKNCA.choose.option(),PKNCA.options()

Examples

## Not run: PKNCA.set.summary(  name="half.life",  description="arithmetic mean and standard deviation",  point=business.mean,  spread=business.sd,  rounding=list(signif=3))## End(Not run)

Separate out a vector of PKNCA imputation methods into a list of functions

Description

An error will be raised if the functions are not found.

Usage

PKNCA_impute_fun_list(x)

Arguments

Details

This function is not for use by users of PKNCA.

Value

A list of character vectors of functions to run.

Methods for imputation of data with PKNCA

Description

Methods for imputation of data with PKNCA

Usage

PKNCA_impute_method_start_conc0(conc, time, start = 0, ..., options = list())PKNCA_impute_method_start_cmin(conc, time, start, end, ..., options = list())PKNCA_impute_method_start_predose(  conc,  time,  start,  end,  conc.group,  time.group,  ...,  max_shift = NA_real_,  options = list())

Arguments

Value

A data.frame with one column named conc with imputed concentrationsand one column named time with the times.

Functions

• PKNCA_impute_method_start_conc0(): Add a new concentration of 0 at the starttime, even if a nonzero concentration exists at that time (usually usedwith single-dose data)

• PKNCA_impute_method_start_cmin(): Add a new concentration of the minimum duringthe interval at the start time (usually used with multiple-dose data)

• PKNCA_impute_method_start_predose(): Shift a predose concentration to become thetime zero concentration (only if a time zero concentration does not exist)

Create a PKNCAconc object

Description

Create a PKNCAconc object

Usage

PKNCAconc(data, ...)## Default S3 method:PKNCAconc(data, ...)## S3 method for class 'tbl_df'PKNCAconc(data, ...)## S3 method for class 'data.frame'PKNCAconc(  data,  formula,  subject,  time.nominal,  exclude = NULL,  duration,  volume,  exclude_half.life,  include_half.life,  sparse = FALSE,  ...,  concu = NULL,  amountu = NULL,  timeu = NULL,  concu_pref = NULL,  amountu_pref = NULL,  timeu_pref = NULL)

Arguments

Value

A PKNCAconc object that can be used for automated NCA.

See Also

Other PKNCA objects:PKNCAdata(),PKNCAdose(),PKNCAresults()

Create a PKNCAdata object.

Description

PKNCAdata() combinesPKNCAconc andPKNCAdose objects and adds in theintervals for PK calculations.

Usage

PKNCAdata(data.conc, data.dose, ...)## S3 method for class 'PKNCAconc'PKNCAdata(data.conc, data.dose, ...)## S3 method for class 'PKNCAdose'PKNCAdata(data.conc, data.dose, ...)## Default S3 method:PKNCAdata(  data.conc,  data.dose,  ...,  formula.conc,  formula.dose,  impute = NA_character_,  intervals,  units,  options = list())

Arguments

Details

Ifdata.dose is not given or isNA, then theintervals must begiven. At least one ofdata.dose andintervals must be given.

Value

A PKNCAdata object with concentration, dose, interval, andcalculation options stored (note that PKNCAdata objects can also haveresults after a NCA calculations are done to the data).

See Also

choose.auc.intervals(),pk.nca(),pknca_units_table()

Other PKNCA objects:PKNCAconc(),PKNCAdose(),PKNCAresults()

Create a PKNCAdose object

Description

Create a PKNCAdose object

Usage

PKNCAdose(data, ...)## Default S3 method:PKNCAdose(data, ...)## S3 method for class 'tbl_df'PKNCAdose(data, ...)## S3 method for class 'data.frame'PKNCAdose(  data,  formula,  route,  rate,  duration,  time.nominal,  exclude = NULL,  ...,  doseu = NULL,  doseu_pref = NULL)

Arguments

Details

Theformula for aPKNCAdose object can begiven three ways: one-sided (missing left side), one-sided (missingright side), or two-sided. Each of the three ways can be givenwith or without groups. When given one-sided missing the leftside, the left side can either be omitted or can be given as aperiod (.):~time|treatment+subject and.~time|treatment+subject are identical, and dose-related NCAparameters will all be reported as not calculable (for example,clearance). When given one-sided missing the right side, the rightside must be specified as a period (.):dose~.|treatment+subject, and only a single row may be givenper group. When the right side is missing, PKNCA assumes that thesame dose is given in every interval. When given as a two-sidedformula

Value

A PKNCAconc object that can be used for automated NCA.

See Also

Other PKNCA objects:PKNCAconc(),PKNCAdata(),PKNCAresults()

Generate a PKNCAresults object

Description

This function should not be run directly. The object is created forsummarization.

Usage

PKNCAresults(result, data, exclude = NULL)

Arguments

Value

A PKNCAresults object with each of the above within.

See Also

Other PKNCA objects:PKNCAconc(),PKNCAdata(),PKNCAdose()

Add columns for calculations within PKNCA intervals

Description

Add columns for calculations within PKNCA intervals

Usage

add.interval.col(  name,  FUN,  values = c(FALSE, TRUE),  unit_type,  pretty_name,  depends = NULL,  desc = "",  sparse = FALSE,  formalsmap = list(),  datatype = c("interval", "individual", "population"))

Arguments

Details

Theformalsmap argument enables mapping some alternate formalargument names to parameters. It is used to generalize functions that mayuse multiple similar arguments (such as the variants of mean residence time).The names of the list should correspond to function formal parameter namesand the values should be one of the following:

• For the current interval:

  character strings of NCA parameter name

  The value of the parameter calculated for the current interval.

  "conc"

  Concentration measurements for the current interval.

  "time"

  Times associated with concentration measurements for the current interval (values start at 0 at the beginning of the current interval).

  "volume"

  Volume associated with concentration measurements for the current interval (typically applies for excretion parameters like urine).

  "duration.conc"

  Durations associated with concentration measurements for the current interval.

  "dose"

  Dose amounts assocuated with the current interval.

  "time.dose"

  Time of dose start associated with the current interval (values start at 0 at the beginning of the current interval).

  "duration.dose"

  Duration of dose (typically infusion duration) for doses in the current interval.

  "route"

  Route of dosing for the current interval.

  "start"

  Time of interval start.

  "end"

  Time of interval end.

  "options"

  PKNCA.options governing calculations.

• For the current group:

  "conc.group"

  Concentration measurements for the current group.

  "time.group"

  Times associated with concentration measurements for the current group (values start at 0 at the beginning of the current interval).

  "volume.group"

  Volume associated with concentration measurements for the current interval (typically applies for excretion parameters like urine).

  "duration.conc.group"

  Durations assocuated with concentration measurements for the current group.

  "dose.group"

  Dose amounts assocuated with the current group.

  "time.dose.group"

  Time of dose start associated with the current group (values start at 0 at the beginning of the current interval).

  "duration.dose.group"

  Duration of dose (typically infusion duration) for doses in the current group.

  "route.group"

  Route of dosing for the current group.

Value

NULL (Calling this function has a side effect of changing theavailable intervals for calculations)

See Also

Other Interval specifications:check.interval.deps(),check.interval.specification(),choose.auc.intervals(),get.interval.cols(),get.parameter.deps()

Examples

## Not run: add.interval.col("cmax",                 FUN="pk.calc.cmax",                 values=c(FALSE, TRUE),                 unit_type="conc",                 pretty_name="Cmax",                 desc="Maximum observed concentration")add.interval.col("cmax.dn",                 FUN="pk.calc.dn",                 values=c(FALSE, TRUE),                 unit_type="conc_dosenorm",                 pretty_name="Cmax (dose-normalized)",                 desc="Maximum observed concentration, dose normalized",                 formalsmap=list(parameter="cmax"),                 depends="cmax")## End(Not run)

Add a hash and associated information to enable checking object provenance.

Description

Add a hash and associated information to enable checking object provenance.

Usage

addProvenance(object, replace = FALSE)

Arguments

Value

The object with provenance as an added item

See Also

checkProvenance()

Add the imputation column to the intervals, if it is not already there

Description

Add the imputation column to the intervals, if it is not already there

Usage

add_impute_to_intervals(object)

Arguments

Value

The PKNCAdata object with an impute column added to the intervals (ifit is not already there) and the object$impute set to that column name

Calculate the adjusted r-squared value

Description

Calculate the adjusted r-squared value

Usage

adj.r.squared(r.sq, n)

Arguments

Value

The numeric adjusted r-squared value

Determine if there are any sparse or dense calculations requested within an interval

Description

Determine if there are any sparse or dense calculations requested within an interval

Usage

any_sparse_dense_in_interval(interval, sparse)

Arguments

Value

A logical value indicating if the interval requests any sparse (ifsparse=TRUE) or dense (ifsparse=FALSE) calculations.

Extract the parameter results from a PKNCAresults and return them as adata.frame.

Description

Extract the parameter results from a PKNCAresults and return them as adata.frame.

Usage

## S3 method for class 'PKNCAresults'as.data.frame(  x,  ...,  out_format = c("long", "wide"),  filter_requested = FALSE,  filter_excluded = FALSE,  out.format = deprecated())

Arguments

Value

A data.frame (or usually a tibble) of results

Convert an object into a PKNCAconc object

Description

Convert an object into a PKNCAconc object

Usage

as_PKNCAconc(x, ...)as_PKNCAdose(x, ...)as_PKNCAdata(x, ...)as_PKNCAresults(x, ...)

Arguments

Value

A converted object

Functions

• as_PKNCAdose(): Convert an object into a PKNCAdose object

• as_PKNCAdata(): Convert an object into a PKNCAdata object

• as_PKNCAresults(): Convert an object into a PKNCAresults object

Generate a sparse_pk object

Description

Generate a sparse_pk object

Usage

as_sparse_pk(conc, time, subject)

Arguments

Value

A sparse_pk object which is a list of lists. The inner lists haveelements named: "time", The time of measurement; "conc", The concentrationmeasured; "subject", The subject identifiers. The object will usually bemodified by future functions to add more named elements to the inner list.

See Also

Other Sparse Methods:pk.calc.sparse_auc(),sparse_auc_weight_linear(),sparse_mean()

Assert that an object is a PKNCAdata object

Description

Assert that an object is a PKNCAdata object

Usage

assert_PKNCAdata(object)

Arguments

Value

The PKNCAdata object (confirmed to be usable)

Assert that a value is a valid AUC method

Description

Assert that a value is a valid AUC method

Usage

assert_aucmethod(method = c("lin up/log down", "linear", "lin-log"))

Arguments

Value

method or an informative error

Verify that concentration measurements are valid

Description

If the concentrations or times are invalid, will provide an error.Reasons for being invalid are

• time is not a number

• conc is not a number

• Anytime value is NA

• time is not monotonically increasing

• conc andtime are not the same length

Usage

assert_conc(conc, any_missing_conc = TRUE)assert_time(time, sorted_time = TRUE)assert_conc_time(conc, time, any_missing_conc = TRUE, sorted_time = TRUE)

Arguments

Details

Some cases may generate warnings but allow the data to proceed.

• A negative concentration is often but not always anerror; it will generate a warning.

Value

conc or give an informative error

time or give an informative error

A data.frame with columns named "conc" and "time" or an informativeerror

Assert that a value is a dosing interval

Description

Assert that a value is a dosing interval

Usage

assert_dosetau(tau)

Arguments

Value

tau or an informative error

Assert Intervals

Description

Verifies that an interval definition is valid for a PKNCAdata object.Valid means that intervals are a data.frame (or data.frame-like object),that the column names are either the groupings of the PKNCAconc part ofthe PKNCAdata object or that they are one of the NCA parameters allowed(i.e. names(get.interval.cols())).It will return the intervals argument unchanged, or it will raise an error.

Usage

assert_intervals(intervals, data)

Arguments

Value

The intervals argument unchanged, or it will raise an error.

Assert that an interval is accurately defined as an interval, and return theinterval

Description

Assert that an interval is accurately defined as an interval, and return theinterval

Usage

assert_intervaltime_single(interval = NULL, start = NULL, end = NULL)

Arguments

Value

interval (orc(start, end))

Assert that a lambda.z value is valid

Description

Assert that a lambda.z value is valid

Usage

assert_lambdaz(  lambda.z,  any.missing = TRUE,  .var.name = checkmate::vname(lambda.z))

Arguments

Value

lambda.z or an informative error

Confirm that a value is greater than another value

Description

Confirm that a value is greater than another value

Usage

assert_number_between(  x,  ...,  na.ok = FALSE,  len = 1,  .var.name = checkmate::vname(x))

Arguments

Value

x or an informative error

Confirm that a value is greater than another value

Description

Confirm that a value is greater than another value

Usage

assert_numeric_between(  x,  any.missing = FALSE,  null.ok = FALSE,  lower_eq = -Inf,  lower = -Inf,  upper = Inf,  upper_eq = Inf,  ...,  .var.name = checkmate::vname(x))

Arguments

Value

x

Assert that a value may either be a column name in the data (first) or asingle unit value (second)

Description

Assert that a value may either be a column name in the data (first) or asingle unit value (second)

Usage

assert_unit_col(unit, data)assert_unit_value(unit)assert_unit(unit, data)

Arguments

Value

unit with an attribute of "unit_type" that is either "column" or"value", orNULL ifis.null(unit)

Functions

• assert_unit_col(): Assert that a column name contains a character string(that could be a unit specification)

• assert_unit_value(): Assert that a value may be a single unit

  The function does not verify that it is a real unit like "ng/mL" only that itis a single character string.

Support function for AUC integration

Description

Support function for AUC integration

Usage

auc_integrate(  conc,  time,  clast,  tlast,  lambda.z,  interval_method,  fun_linear,  fun_log,  fun_inf)

Arguments

Generate functions to do the named function (e.g. mean) applyingthe business rules.

Description

Generate functions to do the named function (e.g. mean) applyingthe business rules.

Usage

business.mean(x, ...)business.sd(x, ...)business.cv(x, ...)business.geomean(x, ...)business.geocv(x, ...)business.min(x, ...)business.max(x, ...)business.median(x, ...)business.range(x, ...)

Arguments

Value

The value of the various functions or NA if too many values aremissing

Functions

• business.sd(): Compute the standard deviation with business rules.

• business.cv(): Compute the coefficient of variation with business rules.

• business.geomean(): Compute the geometric mean with business rules.

• business.geocv(): Compute the geometric coefficient of variation with business rules.

• business.min(): Compute the minimum with business rules.

• business.max(): Compute the maximum with business rules.

• business.median(): Compute the median with business rules.

• business.range(): Compute the range with business rules.

See Also

pk.business()

Check that the conversion to a data type does not change the numberof NA values

Description

Check that the conversion to a data type does not change the numberof NA values

Usage

check.conversion(x, FUN, ...)

Arguments

Value

FUN(x, ...) or an error if the set of NAs change.

Take in a single row of an interval specification and return that row updatedwith any additional calculations that must be done to fulfill alldependencies.

Description

Take in a single row of an interval specification and return that row updatedwith any additional calculations that must be done to fulfill alldependencies.

Usage

check.interval.deps(x)

Arguments

Value

The interval specification with additional calculations added whererequested outputs require them.

See Also

Other Interval specifications:add.interval.col(),check.interval.specification(),choose.auc.intervals(),get.interval.cols(),get.parameter.deps()

Check the formatting of a calculation interval specification data frame.

Description

Calculation interval specifications are data frames defining whatcalculations will be required and summarized from all time intervals. Note:parameters which are not requested may be calculated if it is required for(or computed at the same time as) a requested parameter.

Usage

check.interval.specification(x)

Arguments

Details

start andend time must always be given as columns, and thestart mustbe before theend. Other columns define the parameters to be calculatedand the groupings to apply the intervals to.

Value

x The potentially updated data frame with the interval calculationspecification.

See Also

The vignette "Selection of Calculation Intervals"

Other Interval specifications:add.interval.col(),check.interval.deps(),choose.auc.intervals(),get.interval.cols(),get.parameter.deps()

Check the hash of an object to confirm its provenance.

Description

Check the hash of an object to confirm its provenance.

Usage

checkProvenance(object)

Arguments

Value

TRUE if the provenance is confirmed to be consistent,FALSE ifthe provenance is not consistent, orNA if provenance is not present.

See Also

addProvenance()

Choose intervals to compute AUCs from time and dosing information

Description

Intervals for AUC are selected by the following metrics:

1. If only one dose is administered, use thePKNCA.options("single.dose.aucs")

2. If more than one dose is administered, estimate the AUCbetween any two doses that have PK taken at both of thedosing times and at least one time between the doses.

3. For the final dose of multiple doses, try to determine thedosing interval (\tau) and estimate the AUC in thatinterval if multiple samples are taken in the interval.

4. If there are samples> \tau after the last dose,calculate the half life after the last dose.

Usage

choose.auc.intervals(  time.conc,  time.dosing,  options = list(),  single.dose.aucs = NULL)

Arguments

Value

A data frame with columns forstart,end,auc.type, andhalf.life. Seecheck.interval.specification() for column definitions.The data frame may have zero rows if no intervals could be found.

See Also

pk.calc.auc(),pk.calc.aumc(),pk.calc.half.life(),PKNCA.options()

Other Interval specifications:add.interval.col(),check.interval.deps(),check.interval.specification(),get.interval.cols(),get.parameter.deps()

Other Interval determination:find.tau()

Choose how to interpolate, extrapolate, or integrate data in eachconcentration interval

Description

Choose how to interpolate, extrapolate, or integrate data in eachconcentration interval

Usage

choose_interval_method(conc, time, tlast, method, auc.type, options)

Arguments

Value

A character vector of methods for interpolation/extrapolationmethods that is the same length asconc which indicates how tointerpolate/integrate between each of the concentrations (all but the lastvalue in the vector) and how to extrapolate aftertlast (the last item inthe vector). Possible values in the vector are: 'zero', 'linear', 'log',and 'extrap_log'

Handle BLQ values in the concentration measurements as requested by the user.

Description

Handle BLQ values in the concentration measurements as requested by the user.

Usage

clean.conc.blq(  conc,  time,  ...,  options = list(),  conc.blq = NULL,  conc.na = NULL,  check = TRUE)

Arguments

Details

NA concentrations (and their associated times) will be handled asdescribed inclean.conc.na() before working with the BLQ values. Themethod for handling NA concentrations can affect the output of which pointsare considered BLQ and which are considered "middle". Values areconsidered BLQ if they are 0.

conc.blq can be set either a scalar indicating what should be done forall BLQ values or a list with elements either named "first", "middle" and "last" or "before.tmax" and "after.tmax"each set to a scalar.

The meaning of each of the list elements is:

first

Values up to the first non-BLQ value. Notethat if all values are BLQ, this includes all values.

middle

Values that are BLQ between the first and lastnon-BLQ values.

last

Values that are BLQ after the last non-BLQ value

before.tmax

Values that are BLQ before the time at first maximum concentration

after.tmax

Values that are BLQ after the time at first maximum concentration

The valid settings for each are:

"drop"

Drop the BLQ values

"keep"

Keep the BLQ values

a number

Set the BLQ values to that number

Value

The concentration and time measurements (data frame) filtered andcleaned as requested relative to BLQ in the middle.

See Also

Other Data cleaners:clean.conc.na()

Handle NA values in the concentration measurements as requested by the user.

Description

NA concentrations (and their associated times) will be removed then the BLQvalues in the middle

Usage

clean.conc.na(conc, time, ..., options = list(), conc.na = NULL, check = TRUE)

Arguments

Value

The concentration and time measurements (data frame) filteredand cleaned as requested relative to NA in the concentration.

See Also

Other Data cleaners:clean.conc.blq()

Calculate the covariance for two time points with sparse sampling

Description

The calculation follows equation A3 in Holder 2001 (see references below):

Usage

cov_holder(sparse_pk)

Arguments

Details

\hat{\sigma}_{ij} = \sum\limits_{k=1}^{r_{ij}}{\frac{\left(x_{ik} - \bar{x}_i\right)\left(x_{jk} - \bar{x}_j\right)}{\left(r_{ij} - 1\right) + \left(1 - \frac{r_{ij}}{r_i}\right)\left(1 - \frac{r_{ij}}{r_j}\right)}}

Ifr_{ij} = 0, then\hat{\sigma}_{ij} isdefined as zero (rather than dividing by zero).

Where:

\hat{\sigma}_{ij}

The covariance of times i and j

r_i andr_j

The number of subjects (usually animals) at times i and j, respectively

r_{ij}{r_ij}

The number of subjects (usually animals) at both times i and j

x_{ik} andx_{jk}

The concentration measured for animal k at times i and j, respectively

\bar{x}_i and\bar{x}_j

The mean of the concentrations at times i and j, respectively

The Cauchy-Schwartz inequality is enforced for covariances to keepcorrelation coefficients between -1 and 1, inclusive, as described inequations 8 and 9 of Nedelman and Jia 1998.

Value

A matrix with one row and one column for each element ofsparse_pk_attribute. The covariances are on the off diagonals, and forsimplicity of use, it also calculates the variance on the diagonalelements.

References

Holder DJ. Comments on Nedelman and Jia’s Extension of Satterthwaite’sApproximation Applied to Pharmacokinetics. Journal of BiopharmaceuticalStatistics. 2001;11(1-2):75-79. doi:10.1081/BIP-100104199

Nedelman JR, Jia X. An extension of Satterthwaite’s approximation applied topharmacokinetics. Journal of Biopharmaceutical Statistics. 1998;8(2):317-328.doi:10.1080/10543409808835241

The following functions are defunct

Description

The following functions are defunct

Usage

check.conc.time(...)

Arguments

Functions

• check.conc.time(): Defunct as of version 0.11

Exclude data points or results from calculations or summarization.

Description

Exclude data points or results from calculations or summarization.

Usage

exclude(object, reason, mask, FUN)## Default S3 method:exclude(object, reason, mask, FUN)

Arguments

Details

Only one ofmask orFUN may be given. IfFUN is given, itwill be called with two arguments: a data.frame (or similar object) thatconsists of a single group of the data and the full object (e.g. thePKNCAconc object),FUN(current_group, object), and it must return alogical vector equivalent tomask or a character vector with the reasontext given when data should be excluded orNA_character_ when the datashould be included (for the current exclusion test).

Value

The object with updated information in the exclude column. Theexclude column will contain thereason ifmask orFUN indicate. If aprevious reason for exclusion was given, then subsequent reasons forexclusion will be added to the first with a semicolon space ("; ")separator.

Methods (by class)

• exclude(default): The general case for data exclusion

See Also

Other Result exclusions:exclude_nca

Examples

myconc <- PKNCAconc(data.frame(subject=1,                               time=0:6,                               conc=c(1, 2, 3, 2, 1, 0.5, 0.25)),                    conc~time|subject)exclude(myconc,        reason="Carryover",        mask=c(TRUE, rep(FALSE, 6)))

Exclude NCA parameters based on examining the parameter set.

Description

Exclude NCA parameters based on examining the parameter set.

Usage

exclude_nca_span.ratio(min.span.ratio)exclude_nca_max.aucinf.pext(max.aucinf.pext)exclude_nca_count_conc_measured(  min_count,  exclude_param_pattern = c("^aucall", "^aucinf", "^aucint", "^auciv", "^auclast",    "^aumc", "^sparse_auc"))exclude_nca_min.hl.r.squared(min.hl.r.squared)exclude_nca_min.hl.adj.r.squared(min.hl.adj.r.squared = 0.9)exclude_nca_tmax_early(tmax_early = 0)exclude_nca_tmax_0()

Arguments

Functions

• exclude_nca_span.ratio(): Exclude based on span.ratio

• exclude_nca_max.aucinf.pext(): Exclude based on AUC percent extrapolated (both observed and predicted)

• exclude_nca_count_conc_measured(): Exclude AUC measurements based on count of concentrations measured and not below the lower limit of quantification

• exclude_nca_min.hl.r.squared(): Exclude based on half-life r-squared

• exclude_nca_min.hl.adj.r.squared(): Exclude based on half-life adjusted r-squared

• exclude_nca_tmax_early(): Exclude based on implausibly early Tmax (often used for extravascular dosing with a Tmax value of 0)

• exclude_nca_tmax_0(): Exclude based on implausibly early Tmax (special case fortmax_early = 0)

See Also

Other Result exclusions:exclude()

Examples

my_conc <- PKNCAconc(data.frame(conc=1.1^(3:0),                                time=0:3,                                subject=1),                     conc~time|subject)my_data <- PKNCAdata(my_conc,                     intervals=data.frame(start=0, end=Inf,                                          aucinf.obs=TRUE,                                          aucpext.obs=TRUE))my_result <- pk.nca(my_data)my_result_excluded <- exclude(my_result,                              FUN=exclude_nca_max.aucinf.pext())as.data.frame(my_result_excluded)

Exclude NCA Results Based on Parameter Thresholds

Description

Exclude rows from NCA results based on specified thresholds for a given parameter.This function allows users to define minimum and/or maximum acceptable valuesfor a parameter and excludes rows that fall outside these thresholds.

Usage

exclude_nca_by_param(  parameter,  min_thr = NULL,  max_thr = NULL,  affected_parameters = parameter)

Arguments

Value

A function that can be used withPKNCA::exclude to mark through the 'exclude' columnthe rows in the PKNCA results based on the specified thresholds for a parameter.

Examples

# Example datasetmy_data <- PKNCA::PKNCAdata(  PKNCA::PKNCAconc(data.frame(conc = 5:1,                              time = 0:4,                              subject = 1),                   conc ~ time | subject),  PKNCA::PKNCAdose(data.frame(subject = 1, dose = 100, time = 0),                   dose ~ time | subject))my_result <- PKNCA::pk.nca(my_data)# Exclude rows where span.ratio is less than 2excluded_result <- PKNCA::exclude(  my_result,  FUN = exclude_nca_by_param("span.ratio", min_thr = 2))as.data.frame(excluded_result)

dplyr filtering for PKNCA

Description

dplyr filtering for PKNCA

Usage

## S3 method for class 'PKNCAresults'filter(.data, ..., .preserve = FALSE)## S3 method for class 'PKNCAconc'filter(.data, ..., .preserve = FALSE)## S3 method for class 'PKNCAdose'filter(.data, ..., .preserve = FALSE)

Arguments

See Also

Other dplyr verbs:group_by.PKNCAresults(),inner_join.PKNCAresults(),mutate.PKNCAresults()

Find the repeating interval within a vector of doses

Description

This is intended to find the interval over which x repeats by the ruleunique(mod(x, interval)) is minimized.

Usage

find.tau(x, na.action = stats::na.omit, options = list(), tau.choices = NULL)

Arguments

Value

A scalar indicating the repeating interval with the most repetition.

1. If all values areNA then NA is returned.

2. If all values are the same, then 0 is returned.

3. If all values are equally spaced, then that spacing isreturned.

4. If one of thechoices can minimize the number ofunique values, then that is returned.

5. If none of thechoices can minimize the number ofunique values, then -1 is returned.

See Also

Other Interval determination:choose.auc.intervals()

Find the first occurrence of an operator in a formula and returnthe left, right, or both sides of the operator.

Description

Find the first occurrence of an operator in a formula and returnthe left, right, or both sides of the operator.

Usage

findOperator(x, op, side)

Arguments

Value

The side of the operator requested, NA if requesting the left sideof a unary operator, and NULL if the operator is not found.

See Also

Other Formula parsing:parse_formula_to_cols()

Perform the half-life fit given the data. The function simply fitsthe data without any validation. No selection of points or any othercomponents are done.

Description

Perform the half-life fit given the data. The function simply fitsthe data without any validation. No selection of points or any othercomponents are done.

Usage

fit_half_life(data, tlast, conc_units)

Arguments

Value

A data.frame with one row and columns named "r.squared","adj.r.squared", "PROB", "lambda.z", "clast.pred","lambda.z.n.points", "half.life", "span.ratio"

See Also

pk.calc.half.life()

Extract the formula from a PKNCAconc object.

Description

Extract the formula from a PKNCAconc object.

Usage

## S3 method for class 'PKNCAconc'formula(x, ...)## S3 method for class 'PKNCAdose'formula(x, ...)

Arguments

Value

A formula object

Compute the geometric mean, sd, and CV

Description

Compute the geometric mean, sd, and CV

Usage

geomean(x, na.rm = FALSE)geosd(x, na.rm = FALSE)geocv(x, na.rm = FALSE)

Arguments

Value

The scalar value of the geometric mean, geometric standard deviation,or geometric coefficient of variation.

Functions

• geosd(): Compute the geometric standard deviation,exp(sd(log(x))).

• geocv(): Compute the geometric coefficient of variation,sqrt(exp(sd(log(x))^2)-1)*100.

References

Kirkwood T. B.L. Geometric means and measures of dispersion.Biometrics 1979; 35: 908-909

Examples

geomean(1:3)geosd(1:3)geocv(1:3)

Extract the best model from a list of models using the AIC.

Description

Extract the best model from a list of models using the AIC.

Usage

get.best.model(object, ...)

Arguments

Value

The model which is assessed as best. If more than one are equal,the first is chosen.

Get the first model from a list of models

Description

Get the first model from a list of models

Usage

get.first.model(object)

Arguments

Value

The first item in theobject that is not a list orNA. IfNAis passed in or the list (of lists) is allNA, thenNA is returned.

Get the columns that can be used in an interval specification

Description

Get the columns that can be used in an interval specification

Usage

get.interval.cols()

Value

A list with named elements for each parameter. Each list elementcontains the parameter definition.

See Also

check.interval.specification() and the vignette "Selection ofCalculation Intervals"

Other Interval specifications:add.interval.col(),check.interval.deps(),check.interval.specification(),choose.auc.intervals(),get.parameter.deps()

Examples

get.interval.cols()

Get all columns that depend on a parameter

Description

Get all columns that depend on a parameter

Usage

get.parameter.deps(x)

Arguments

Value

A character vector of parameter names that depend on the parameterx. If none depend onx, then the result will be an empty vector.

See Also

Other Interval specifications:add.interval.col(),check.interval.deps(),check.interval.specification(),choose.auc.intervals(),get.interval.cols()

Retrieve the value of an attribute column.

Description

Retrieve the value of an attribute column.

Usage

getAttributeColumn(object, attr_name, warn_missing = c("attr", "column"))

Arguments

Value

The value of the attribute (orNULL if the attribute is not set orthe column does not exist)

Get the value from a column in a data frame if the value is a columnthere, otherwise, the value should be a scalar or the length of thedata.

Description

Get the value from a column in a data frame if the value is a columnthere, otherwise, the value should be a scalar or the length of thedata.

Usage

getColumnValueOrNot(data, value, prefix = "X")

Arguments

Value

A list with elements named "data", "name" giving thedata with acolumn named "name" with the value in that column.

Get the name of the element containing the data for the currentobject.

Description

Get the name of the element containing the data for the currentobject.

Usage

## S3 method for class 'PKNCAconc'getDataName(object)## S3 method for class 'PKNCAdose'getDataName(object)## S3 method for class 'PKNCAresults'getDataName(object)getDataName(object)## Default S3 method:getDataName(object)

Arguments

Value

A character scalar with the name of the data object (orNULL if themethod does not apply).

Methods (by class)

• getDataName(default): If no data name exists, returns NULL.

See Also

Other PKNCA object extractors:getDepVar(),getIndepVar()

Get the dependent variable (left hand side of the formula) from aPKNCA object.

Description

Get the dependent variable (left hand side of the formula) from aPKNCA object.

Usage

getDepVar(x, ...)

Arguments

Value

The vector of the dependent variable from the object.

See Also

Other PKNCA object extractors:getDataName.PKNCAconc(),getIndepVar()

Get the groups (right hand side after the| from a PKNCAobject).

Description

Get the groups (right hand side after the| from a PKNCAobject).

Get the groups (right hand side after the| from a PKNCAobject).

Usage

## S3 method for class 'PKNCAconc'getGroups(  object,  form = stats::formula(object),  level,  data = as.data.frame(object),  sep)## S3 method for class 'PKNCAdata'getGroups(object, ...)## S3 method for class 'PKNCAdose'getGroups(...)## S3 method for class 'PKNCAresults'getGroups(  object,  form = formula(object$data$conc),  level,  data = object$result,  sep)

Arguments

Value

A data frame with the (selected) group columns.

A data frame with the (selected) group columns.

Get the independent variable (right hand side of the formula) froma PKNCA object.

Description

Get the independent variable (right hand side of the formula) froma PKNCA object.

Usage

getIndepVar(x, ...)

Arguments

Value

The vector of the independent variable from the object.

See Also

Other PKNCA object extractors:getDataName.PKNCAconc(),getDepVar()

Determine which concentrations were used for half-life calculation

Description

Determine which concentrations were used for half-life calculation

Usage

get_halflife_points(object)

Arguments

Value

A logical vector withTRUE if the point was used for half-life(including concentrations below the limit of quantification within therange of times for calculation),FALSE if it was not used for half-lifebut the half-life was calculated for the interval, andNA if half-lifewas not calculated for the interval. If a row is excluded from allcalculations, it is set toNA as well.

Examples

o_conc <- PKNCAconc(Theoph, conc~Time|Subject)o_data <- PKNCAdata(o_conc, intervals = data.frame(start = 0, end = Inf, half.life = TRUE))o_nca <- pk.nca(o_data)get_halflife_points(o_nca)

Get the impute function from either the intervals column or from the method

Description

Get the impute function from either the intervals column or from the method

Usage

get_impute_method(intervals, impute)

Arguments

Value

The imputation function vector

dplyr grouping for PKNCA

Description

dplyr grouping for PKNCA

Usage

## S3 method for class 'PKNCAresults'group_by(.data, ..., .add = FALSE, .drop = dplyr::group_by_drop_default(.data))## S3 method for class 'PKNCAconc'group_by(.data, ..., .add = FALSE, .drop = dplyr::group_by_drop_default(.data))## S3 method for class 'PKNCAdose'group_by(.data, ..., .add = FALSE, .drop = dplyr::group_by_drop_default(.data))## S3 method for class 'PKNCAresults'ungroup(x, ...)## S3 method for class 'PKNCAconc'ungroup(x, ...)## S3 method for class 'PKNCAdose'ungroup(x, ...)

Arguments

See Also

Other dplyr verbs:filter.PKNCAresults(),inner_join.PKNCAresults(),mutate.PKNCAresults()

Get grouping variables for a PKNCA object

Description

Get grouping variables for a PKNCA object

Usage

## S3 method for class 'PKNCAconc'group_vars(x)## S3 method for class 'PKNCAdata'group_vars(x)## S3 method for class 'PKNCAdose'group_vars(x)## S3 method for class 'PKNCAresults'group_vars(x)

Arguments

Value

A character vector (possibly empty) of the grouping variables

Functions

• group_vars(PKNCAdata): Get group_vars for a PKNCAdata objectfrom the PKNCAconc object within

• group_vars(PKNCAdose): Get group_vars for a PKNCAdose object

• group_vars(PKNCAresults): Get group_vars for a PKNCAresults objectfrom the PKNCAconc object within

dplyr joins for PKNCA

Description

dplyr joins for PKNCA

Usage

## S3 method for class 'PKNCAresults'inner_join(  x,  y,  by = NULL,  copy = FALSE,  suffix = c(".x", ".y"),  ...,  keep = FALSE)## S3 method for class 'PKNCAresults'left_join(  x,  y,  by = NULL,  copy = FALSE,  suffix = c(".x", ".y"),  ...,  keep = FALSE)## S3 method for class 'PKNCAresults'right_join(  x,  y,  by = NULL,  copy = FALSE,  suffix = c(".x", ".y"),  ...,  keep = FALSE)## S3 method for class 'PKNCAresults'full_join(  x,  y,  by = NULL,  copy = FALSE,  suffix = c(".x", ".y"),  ...,  keep = FALSE)## S3 method for class 'PKNCAconc'inner_join(  x,  y,  by = NULL,  copy = FALSE,  suffix = c(".x", ".y"),  ...,  keep = FALSE)## S3 method for class 'PKNCAconc'left_join(  x,  y,  by = NULL,  copy = FALSE,  suffix = c(".x", ".y"),  ...,  keep = FALSE)## S3 method for class 'PKNCAconc'right_join(  x,  y,  by = NULL,  copy = FALSE,  suffix = c(".x", ".y"),  ...,  keep = FALSE)## S3 method for class 'PKNCAconc'full_join(  x,  y,  by = NULL,  copy = FALSE,  suffix = c(".x", ".y"),  ...,  keep = FALSE)## S3 method for class 'PKNCAdose'inner_join(  x,  y,  by = NULL,  copy = FALSE,  suffix = c(".x", ".y"),  ...,  keep = FALSE)## S3 method for class 'PKNCAdose'left_join(  x,  y,  by = NULL,  copy = FALSE,  suffix = c(".x", ".y"),  ...,  keep = FALSE)## S3 method for class 'PKNCAdose'right_join(  x,  y,  by = NULL,  copy = FALSE,  suffix = c(".x", ".y"),  ...,  keep = FALSE)## S3 method for class 'PKNCAdose'full_join(  x,  y,  by = NULL,  copy = FALSE,  suffix = c(".x", ".y"),  ...,  keep = FALSE)

Arguments

See Also

Other dplyr verbs:filter.PKNCAresults(),group_by.PKNCAresults(),mutate.PKNCAresults()

Interpolate concentrations between measurements or extrapolate concentrationsafter the last measurement.

Description

interpolate.conc() andextrapolate.conc() returns an interpolated (orextrapolated) concentration.interp.extrap.conc() will choose whetherinterpolation or extrapolation is required and will also operate on manyconcentrations. These will typically be used to estimate the concentrationbetween two measured concentrations or after the last measured concentration.Of note, these functions will not extrapolate prior to the first point.

Usage

interp.extrap.conc(  conc,  time,  time.out,  lambda.z = NA,  clast = pk.calc.clast.obs(conc, time),  options = list(),  method = NULL,  auc.type = "AUCinf",  interp.method,  extrap.method,  ...,  conc.blq = NULL,  conc.na = NULL,  check = TRUE)interpolate.conc(  conc,  time,  time.out,  options = list(),  method = NULL,  interp.method,  conc.blq = NULL,  conc.na = NULL,  conc.origin = 0,  ...,  check = TRUE)extrapolate.conc(  conc,  time,  time.out,  lambda.z = NA,  clast = pk.calc.clast.obs(conc, time),  auc.type = "AUCinf",  extrap.method,  options = list(),  conc.na = NULL,  conc.blq = NULL,  ...,  check = TRUE)interp.extrap.conc.dose(  conc,  time,  time.dose,  route.dose = "extravascular",  duration.dose = NA,  time.out,  out.after = FALSE,  options = list(),  conc.blq = NULL,  conc.na = NULL,  ...,  check = TRUE)

Arguments

Details

AnNA value for thelambda.z parameter will prevent extrapolation.

extrap.method

'AUCinf'

Use lambda.z to extrapolate beyond the last point with the half-life.

'AUCall'

If the last point is above the limit of quantification or missing, this is identical to 'AUCinf'. If the last point is below the limit of quantification, then linear interpolation between the Clast and the next BLQ is used for that interval and all additional points are extrapolated as 0.

'AUClast'

Extrapolates all points after the last above the limit of quantification as 0.

duration.dose anddirection.out are ignored ifroute.dose == "extravascular".direction.out is ignored ifduration.dose > 0.

route.dose andduration.dose affect how interpolation/extrapolation ofthe concentration occurs at the time of dosing. Ifroute.dose == "intravascular" andduration.dose == 0 then extrapolation occurs for an IVbolus usingpk.calc.c0() with the data after dosing. Otherwise (eitherroute.dose == "extravascular" orduration.dose > 0), extrapolation occursusing the concentrations before dosing and estimating the half-life (or moreprecisely, estimatinglambda.z). Finally,direction.out can change thedirection of interpolation in cases withroute.dose == "intravascular" andduration.dose == 0. Whendirection.out == "before" interpolation occursonly with data before the dose (as is the case forroute.dose == "extravascular"), but ifdirection.out == "after" interpolation occursfrom the data after dosing.

Value

The interpolated or extrapolated concentration value as a scalardouble (or vector forinterp.extrap.conc()).

Functions

• interpolate.conc(): Interpolate concentrations through Tlast (inclusive)

• extrapolate.conc(): Extrapolate concentrations after Tlast

• interp.extrap.conc.dose(): Interpolate and extrapolate concentrationswithout interpolating or extrapolating beyond doses.

See Also

pk.calc.clast.obs(),pk.calc.half.life(),pk.calc.c0()

Interpolate or extrapolate concentrations using the provided method

Description

Interpolate or extrapolate concentrations using the provided method

Usage

interpolate_conc_linear(conc_1, conc_2, time_1, time_2, time_out)interpolate_conc_log(conc_1, conc_2, time_1, time_2, time_out)extrapolate_conc_lambdaz(clast, lambda.z, tlast, time_out)

Arguments

Value

The interpolated or extrapolated value using the correct method

Is a PKNCA object used for sparse PK?

Description

Is a PKNCA object used for sparse PK?

Usage

## S3 method for class 'PKNCAconc'is_sparse_pk(object)## S3 method for class 'PKNCAdata'is_sparse_pk(object)## S3 method for class 'PKNCAresults'is_sparse_pk(object)is_sparse_pk(object)

Arguments

Value

TRUE if sparse andFALSE if dense (not sparse)

Extract the columns used in the formula (in order) from a PKNCAconcor PKNCAdose object.

Description

Extract the columns used in the formula (in order) from a PKNCAconcor PKNCAdose object.

Usage

## S3 method for class 'PKNCAconc'model.frame(formula, ...)## S3 method for class 'PKNCAdose'model.frame(formula, ...)

Arguments

Value

A data frame with the columns from the object in formula order.

dplyr mutate-based modification for PKNCA

Description

dplyr mutate-based modification for PKNCA

Usage

## S3 method for class 'PKNCAresults'mutate(.data, ...)## S3 method for class 'PKNCAconc'mutate(.data, ...)## S3 method for class 'PKNCAdose'mutate(.data, ...)

Arguments

See Also

Other dplyr verbs:filter.PKNCAresults(),group_by.PKNCAresults(),inner_join.PKNCAresults()

Normalize the exclude column by setting blanks to NA

Description

Normalize the exclude column by setting blanks to NA

Usage

normalize_exclude(object)

Arguments

Value

The exclude vector whereNA indicates not to exclude and anythingelse indicates to exclude.

Convert a formula representation to the columns for input data

Description

Convert a formula representation to the columns for input data

Usage

parse_formula_to_cols(form)

Arguments

Value

A list of column names for various formula parts

See Also

Other Formula parsing:findOperator()

Run any function with a maximum missing fraction of X and 0s possiblycounting as missing. The maximum fraction missing comes fromPKNCA.options("max.missing").

Description

Note that all missing values are removed prior to calling the function.

Usage

pk.business(FUN, zero.missing = FALSE, max.missing)

Arguments

Value

A version of FUN that can be called with parameters that are checkedfor missingness (and zeros) with missing (and zeros) removed before thecall. Ifmax.missing is exceeded, then NA is returned.

Examples

my_mean <- pk.business(FUN=mean)mean(c(1:3, NA))# Less than half missing results in the summary statistic of the available# values.my_mean(c(1:3, NA))# More than half missing results in a missing valuemy_mean(c(1:3, rep(NA, 4)))

Calculate amount excreted (typically in urine or feces)

Description

Calculate amount excreted (typically in urine or feces)

Usage

pk.calc.ae(conc, volume, check = TRUE)

Arguments

Details

ae issum(conc*volume).

The units for the concentration and volume should match such thatsum(conc*volume) has units of mass or moles.

Value

The amount excreted during the interval

See Also

pk.calc.clr(),pk.calc.fe()

Calculate the AUC above a given concentration

Description

Concentrations below the given concentration (conc_above) will be setto zero.

Usage

pk.calc.aucabove(conc, time, conc_above = NA_real_, ..., options = list())

Arguments

Value

The AUC of the concentration above the limit

Calculate the AUC over an interval with interpolation and/orextrapolation of concentrations for the beginning and end of theinterval.

Description

Calculate the AUC over an interval with interpolation and/orextrapolation of concentrations for the beginning and end of theinterval.

Usage

pk.calc.aucint(  conc,  time,  interval = NULL,  start = NULL,  end = NULL,  clast = pk.calc.clast.obs(conc, time),  lambda.z = NA,  time.dose = NULL,  route = "extravascular",  duration.dose = 0,  method = NULL,  auc.type = "AUClast",  conc.blq = NULL,  conc.na = NULL,  check = TRUE,  ...,  options = list())pk.calc.aucint.last(  conc,  time,  start = NULL,  end = NULL,  time.dose,  ...,  options = list())pk.calc.aucint.all(  conc,  time,  start = NULL,  end = NULL,  time.dose,  ...,  options = list())pk.calc.aucint.inf.obs(  conc,  time,  start = NULL,  end = NULL,  time.dose,  lambda.z,  clast.obs,  ...,  options = list())pk.calc.aucint.inf.pred(  conc,  time,  start = NULL,  end = NULL,  time.dose,  lambda.z,  clast.pred,  ...,  options = list())

Arguments

Details

Whenpk.calc.aucint() needs to extrapolate usinglambda.z (in otherwords, using the half-life), it will always extrapolate using the logarithmictrapezoidal rule to align with using a half-life calculation for theextrapolation.

Value

The AUC for an interval of time as a number

Functions

• pk.calc.aucint.last(): Interpolate or extrapolate concentrations forAUClast

• pk.calc.aucint.all(): Interpolate or extrapolate concentrations forAUCall

• pk.calc.aucint.inf.obs(): Interpolate or extrapolate concentrations forAUCinf.obs

• pk.calc.aucint.inf.pred(): Interpolate or extrapolate concentrations forAUCinf.pred

See Also

PKNCA.options(),interp.extrap.conc.dose()

Other AUC calculations:pk.calc.auxc()

Calculate AUC for intravenous dosing

Description

Calculate AUC for intravenous dosing

Usage

pk.calc.auciv(conc, time, c0, auc, ..., options = list(), check = TRUE)pk.calc.auciv_pbext(auc, auciv)

Arguments

Details

The AUC for intravenous (IV) dosing extrapolates the AUC back fromthe first measurement to time 0 using c0 and the AUC calculated by anothermethod (for example the auclast).

The calculation method takes the following steps:

• time = 0 must be present in the data with a measured concentration.

• The AUC betweentime = 0 and the next time point is calculated (auc_first).

• The AUC betweentime = 0 withc0 and the next time point is calculated (auc_second).

• The final AUC is the initial AUC plus the difference between the two AUCs (auc_final <- auc + auc_second - auc_first).

The calculation for back-extrapolation is100*(1 - auc/auciv).

Value

pk.calc.auciv: The AUC calculated usingc0

pk.calc.auciv_pctbackextrap: The AUC percent back-extrapolated

Functions

• pk.calc.auciv_pbext(): Calculate the percent back-extrapolated AUC for IVadministration

Calculate the AUC percent extrapolated

Description

Calculate the AUC percent extrapolated

Usage

pk.calc.aucpext(auclast, aucinf)

Arguments

Details

aucpext is100*(1-auclast/aucinf).

Value

The numeric value of the AUC percent extrapolated orNA_real_ ifany of the following are trueis.na(aucinf),is.na(auclast),aucinf <= 0, orauclast <= 0.

A compute the Area Under the (Moment) Curve

Description

Compute the area under the curve (AUC) and the area under the moment curve(AUMC) for pharmacokinetic (PK) data. AUC and AUMC are used for manypurposes when analyzing PK in drug development.

Usage

pk.calc.auxc(  conc,  time,  interval = c(0, Inf),  clast = pk.calc.clast.obs(conc, time, check = FALSE),  lambda.z = NA,  auc.type = c("AUClast", "AUCinf", "AUCall"),  options = list(),  method = NULL,  conc.blq = NULL,  conc.na = NULL,  check = TRUE,  fun_linear,  fun_log,  fun_inf)pk.calc.auc(conc, time, ..., options = list())pk.calc.auc.last(conc, time, ..., options = list())pk.calc.auc.inf(conc, time, ..., options = list(), lambda.z)pk.calc.auc.inf.obs(conc, time, clast.obs, ..., options = list(), lambda.z)pk.calc.auc.inf.pred(conc, time, clast.pred, ..., options = list(), lambda.z)pk.calc.auc.all(conc, time, ..., options = list())pk.calc.aumc(conc, time, ..., options = list())pk.calc.aumc.last(conc, time, ..., options = list())pk.calc.aumc.inf(conc, time, ..., options = list(), lambda.z)pk.calc.aumc.inf.obs(conc, time, clast.obs, ..., options = list(), lambda.z)pk.calc.aumc.inf.pred(conc, time, clast.pred, ..., options = list(), lambda.z)pk.calc.aumc.all(conc, time, ..., options = list())

Arguments

Details

pk.calc.auc.last is simply a shortcut setting theintervalparameter toc(0, "last").

Extrapolation beyond Clast occurs using the half-life and Clast,obs;Clast,pred is not yet supported.

If all conc input are zero, then the AU(M)C is zero.

You probably do not want to callpk.calc.auxc(). Usually, you will callone of the other functions for calculating AUC likepk.calc.auc.last(),pk.calc.auc.inf.obs(), etc.

Value

A numeric value for the AU(M)C.

Functions

• pk.calc.auc(): Compute the area under the curve

• pk.calc.auc.last(): Compute the AUClast.

• pk.calc.auc.inf(): Compute the AUCinf

• pk.calc.auc.inf.obs(): Compute the AUCinf with the observed Clast.

• pk.calc.auc.inf.pred(): Compute the AUCinf with the predicted Clast.

• pk.calc.auc.all(): Compute the AUCall.

• pk.calc.aumc(): Compute the area under the moment curve

• pk.calc.aumc.last(): Compute the AUMClast.

• pk.calc.aumc.inf(): Compute the AUMCinf

• pk.calc.aumc.inf.obs(): Compute the AUMCinf with the observed Clast.

• pk.calc.aumc.inf.pred(): Compute the AUMCinf with the predicted Clast.

• pk.calc.aumc.all(): Compute the AUMCall.

References

Gabrielsson J, Weiner D. "Section 2.8.1 Computation methods - Lineartrapezoidal rule." Pharmacokinetic & Pharmacodynamic Data Analysis: Conceptsand Applications, 4th Edition. Stockholm, Sweden: Swedish PharmaceuticalPress, 2000. 162-4.

Gabrielsson J, Weiner D. "Section 2.8.3 Computation methods - Log-lineartrapezoidal rule." Pharmacokinetic & Pharmacodynamic Data Analysis: Conceptsand Applications, 4th Edition. Stockholm, Sweden: Swedish PharmaceuticalPress, 2000. 164-7.

See Also

clean.conc.blq()

Other AUC calculations:pk.calc.aucint()

Examples

myconc <- c(0, 1, 2, 1, 0.5, 0.25, 0)mytime <- c(0, 1, 2, 3, 4,   5,    6)pk.calc.auc(myconc, mytime, interval=c(0, 6))pk.calc.auc(myconc, mytime, interval=c(0, Inf))

Estimate the concentration at dosing time for an IV bolus dose.

Description

Estimate the concentration at dosing time for an IV bolus dose.

Usage

pk.calc.c0(  conc,  time,  time.dose = 0,  method = c("c0", "logslope", "c1", "cmin", "set0"),  check = TRUE)pk.calc.c0.method.logslope(conc, time, time.dose = 0, check = TRUE)pk.calc.c0.method.c0(conc, time, time.dose = 0, check = TRUE)pk.calc.c0.method.c1(conc, time, time.dose = 0, check = TRUE)pk.calc.c0.method.set0(conc, time, time.dose = 0, check = TRUE)pk.calc.c0.method.cmin(conc, time, time.dose = 0, check = TRUE)

Arguments

Details

Methods available for interpolation are below, and eachhas its own specific function.

c0

If the observedconc attime.dose is nonzero, return that. This method should usually be used first for single-dose IV bolus data in case nominal time zero is measured.

logslope

Compute the semilog line between the first two measured times, and use that line to extrapolate backward totime.dose

c1

Use the first point aftertime.dose

cmin

Set c0 to cmin during the interval. This method should usually be used for multiple-dose oral data and IV infusion data.

set0

Set c0 to zero (regardless of any other data). This method should usually be used first for single-dose oral data.

Value

The estimated concentration at time 0.

Functions

• pk.calc.c0.method.logslope(): Semilog regress the first and second pointsafter time.dose. This method will returnNA if the secondconc aftertime.dose is 0 or greater than the first.

• pk.calc.c0.method.c0(): UseC0 =conc[time %in% time.dose] if it isnonzero.

• pk.calc.c0.method.c1(): UseC0 =C1.

• pk.calc.c0.method.set0(): UseC0 = 0 (typically used for single dose oral andIV infusion)

• pk.calc.c0.method.cmin(): UseC0 = Cmin (typically used for multiple dose oraland IV infusion but not IV bolus)

Calculate the average concentration during an interval.

Description

Calculate the average concentration during an interval.

Usage

pk.calc.cav(auc, start, end)

Arguments

Details

cav isauc/(end-start).

Value

The Cav (average concentration during the interval)

Determine the concentration at the end of infusion

Description

Determine the concentration at the end of infusion

Usage

pk.calc.ceoi(conc, time, duration.dose = NA, check = TRUE)

Arguments

Value

The concentration at the end of the infusion,NA ifduration.dose isNA, orNA if alltime != duration.dose

Calculate the (observed oral) clearance

Description

Calculate the (observed oral) clearance

Usage

pk.calc.cl(dose, auc)

Arguments

Details

cl isdose/auc.

Ifdose is the same length as the other inputs, then the outputwill be the same length as all of the inputs; the function assumes that youare calculating for multiple intervals simultaneously. If the inputs otherthandose are scalars anddose is a vector, then the function assumesmultiple doses were given in a single interval, and the sum of thedoseswill be used for the calculation.

Value

the numeric value of the total (CL) or observed oral clearance(CL/F)

References

Gabrielsson J, Weiner D. "Section 2.5.1 Derivation of clearance."Pharmacokinetic & Pharmacodynamic Data Analysis: Concepts and Applications,4th Edition. Stockholm, Sweden: Swedish Pharmaceutical Press, 2000. 86-7.

Determine the last observed concentration above the limit of quantification(LOQ).

Description

If all concentrations are missing,NA_real_ is returned. If allconcentrations are zero (below the limit of quantification) or missing, zerois returned. If Tlast is NA (due to no non-missing above LOQ measurements),this will returnNA_real_.

Usage

pk.calc.clast.obs(conc, time, check = TRUE)

Arguments

Value

The last observed concentration above the LOQ

See Also

Other NCA parameters for concentrations during the intervals:pk.calc.cmax(),pk.calc.count_conc(),pk.calc.cstart(),pk.calc.ctrough()

Calculate renal clearance

Description

Calculate renal clearance

Usage

pk.calc.clr(ae, auc)

Arguments

Details

clr issum(ae)/auc.

The units for theae andauc should match such thatae/auc hasunits of volume/time.

Value

The renal clearance as a number

See Also

pk.calc.ae(),pk.calc.fe()

Determine maximum observed PK concentration

Description

Determine maximum observed PK concentration

Usage

pk.calc.cmax(conc, check = TRUE)pk.calc.cmin(conc, check = TRUE)

Arguments

Value

a number for the maximum concentration or NA if all concentrationsare missing

Functions

• pk.calc.cmin(): Determine the minimum observed PKconcentration

See Also

Other NCA parameters for concentrations during the intervals:pk.calc.clast.obs(),pk.calc.count_conc(),pk.calc.cstart(),pk.calc.ctrough()

Other NCA parameters for concentrations during the intervals:pk.calc.clast.obs(),pk.calc.count_conc(),pk.calc.cstart(),pk.calc.ctrough()

Examples

conc_data <- Theoph[Theoph$Subject == 1,]pk.calc.cmin(conc_data$conc)

Count the number of concentration measurements in an interval

Description

count_conc andcount_conc_measured are typically used for quality controlon the data to ensure that there are a sufficient number of non-missingsamples for a calculation and to ensure that data are consistent betweenindividuals.

Usage

pk.calc.count_conc(conc, check = TRUE)pk.calc.count_conc_measured(conc, check = TRUE)

Arguments

Value

a count of the non-missing concentrations (0 if all concentrationsare missing)

a count of the non-missing, measured (not below or above the limitof quantification) concentrations (0 if all concentrations are missing)

Functions

• pk.calc.count_conc_measured(): Count the number of concentration measurementsthat are not missing, above, or below the limit of quantification in aninterval

See Also

Other NCA parameters for concentrations during the intervals:pk.calc.clast.obs(),pk.calc.cmax(),pk.calc.cstart(),pk.calc.ctrough()

Determine the concentration at the beginning of the interval

Description

Determine the concentration at the beginning of the interval

Usage

pk.calc.cstart(conc, time, start)

Arguments

Value

The concentration whentime == end. If none match, thenNA

See Also

Other NCA parameters for concentrations during the intervals:pk.calc.clast.obs(),pk.calc.cmax(),pk.calc.count_conc(),pk.calc.ctrough()

Determine the trough (end of interval) concentration

Description

Determine the trough (end of interval) concentration

Usage

pk.calc.ctrough(conc, time, end)

Arguments

Value

The concentration whentime == end. If none match, thenNA

See Also

Other NCA parameters for concentrations during the intervals:pk.calc.clast.obs(),pk.calc.cmax(),pk.calc.count_conc(),pk.calc.cstart()

Determine the degree of fluctuation

Description

Determine the degree of fluctuation

Usage

pk.calc.deg.fluc(cmax, cmin, cav)

Arguments

Details

deg.fluc is100*(cmax - cmin)/cav.

Value

The degree of fluctuation around the average concentration.

Determine dose normalized NCA parameter

Description

Determine dose normalized NCA parameter

Usage

pk.calc.dn(parameter, dose)

Arguments

Value

a number for dose normalized AUC

Examples

pk.calc.dn(90, 10)

Calculate the absolute (or relative) bioavailability

Description

Calculate the absolute (or relative) bioavailability

Usage

pk.calc.f(dose1, auc1, dose2, auc2)

Arguments

Details

f is(auc2/dose2)/(auc1/dose1).

Calculate fraction excreted (typically in urine or feces)

Description

Calculate fraction excreted (typically in urine or feces)

Usage

pk.calc.fe(ae, dose)

Arguments

Details

fe issum(ae)/dose

The units forae anddose should be the same so thatae/doseis a unitless fraction.

Value

The fraction of dose excreted

See Also

pk.calc.ae(),pk.calc.clr()

Compute the half-life and associated parameters

Description

The terminal elimination half-life is estimated from the final points in theconcentration-time curve using semi-log regression (log(conc)~time)with automated selection of the points for calculation (unlessmanually.selected.points isTRUE).

Usage

pk.calc.half.life(  conc,  time,  tmax,  tlast,  time.dose = NULL,  duration.dose = 0,  manually.selected.points = FALSE,  options = list(),  min.hl.points = NULL,  adj.r.squared.factor = NULL,  conc.blq = NULL,  conc.na = NULL,  first.tmax = NULL,  allow.tmax.in.half.life = NULL,  check = TRUE)

Arguments

Details

See the "Half-Life Calculation" vignette for more details on the calculationmethods used.

Ifmanually.selected.points isFALSE (default), thehalf-life is calculated by computing the best fit line for all points at orafter tmax (based on the value ofallow.tmax.in.half.life). The besthalf-life is chosen by the following rules in order:

• At leastmin.hl.points points included

• Alambda.z > 0 and at the same time the best adjusted r-squared(withinadj.r.squared.factor)

• The one with the most points included

Ifmanually.selected.points isTRUE, theconcandtime data are used as-is without any form of selection forthe best-fit half-life.

Value

A data frame with one row and columns for

tmax

Time of maximum observed concentration (only includedif not given as an input)

tlast

Time of last observed concentration above the LOQ (onlyincluded if not given as an input)

r.squared

coefficient of determination

adj.r.squared

adjusted coefficient of determination

lambda.z

elimination rate

lambda.z.time.first

first time for half-life calculation

lambda.z.time.last

last time for half-life calculation

lambda.z.n.points

number of points in half-life calculation

clast.pred

Concentration at tlast as predicted by the half-lifeline

half.life

half-life

span.ratio

span ratio [ratio of half-life to time used forhalf-life calculation

References

Gabrielsson J, Weiner D. "Section 2.8.4 Strategies for estimation oflambda-z." Pharmacokinetic & Pharmacodynamic Data Analysis: Conceptsand Applications, 4th Edition. Stockholm, Sweden: SwedishPharmaceutical Press, 2000. 167-9.

Calculate the elimination rate (Kel)

Description

Calculate the elimination rate (Kel)

Usage

pk.calc.kel(mrt)

Arguments

Value

the numeric value of the elimination rate

Calculate the mean residence time (MRT) for single-dose data or linearmultiple-dose data.

Description

Calculate the mean residence time (MRT) for single-dose data or linearmultiple-dose data.

Usage

pk.calc.mrt(auc, aumc)pk.calc.mrt.iv(auc, aumc, duration.dose)

Arguments

Details

mrt isaumc/auc - duration.dose/2 whereduration.dose = 0 for oral administration.

Value

the numeric value of the mean residence time

Functions

• pk.calc.mrt.iv(): MRT for an IV infusion

See Also

pk.calc.mrt.md()

Calculate the mean residence time (MRT) for multiple-dose data with nonlinearkinetics.

Description

Calculate the mean residence time (MRT) for multiple-dose data with nonlinearkinetics.

Usage

pk.calc.mrt.md(auctau, aumctau, aucinf, tau)

Arguments

Details

mrt.md isaumctau/auctau + tau*(aucinf-auctau)/auctau and shouldonly be used for multiple dosing with equal intervals between doses.

Note that ifaucinf == auctau (as would be the assumption withlinear kinetics), the equation becomes the same as the single-dose MRT.

See Also

pk.calc.mrt()

Determine the peak-to-trough ratio

Description

Determine the peak-to-trough ratio

Usage

pk.calc.ptr(cmax, ctrough)

Arguments

Details

ptr iscmax/ctrough.

Value

The ratio of cmax to ctrough (if ctrough == 0, NA)

Calculate AUC and related parameters using sparse NCA methods

Description

The AUC is calculated as:

Usage

pk.calc.sparse_auc(  conc,  time,  subject,  method = NULL,  auc.type = "AUClast",  ...,  options = list())pk.calc.sparse_auclast(conc, time, subject, ..., options = list())

Arguments

Details

AUC=\sum\limits_{i} w_i \bar{C}_i

Where:

AUC

is the estimated area under the concentration-time curve

w_i

is the weight applied to the concentration at time i (related to the time which it affects, seesparse_auc_weight_linear())

\bar{C}_i

is the average concentration at time i

Functions

• pk.calc.sparse_auclast(): Compute the AUClast for sparse PK

See Also

Other Sparse Methods:as_sparse_pk(),sparse_auc_weight_linear(),sparse_mean()

Determine the PK swing

Description

Determine the PK swing

Usage

pk.calc.swing(cmax, cmin)

Arguments

Details

swing is100*(cmax - cmin)/cmin.

Value

The swing above the minimum concentration. Ifcmin is zero, thenthe result is infinity.

Calculate the effective half-life

Description

Calculate the effective half-life

Usage

pk.calc.thalf.eff(mrt)

Arguments

Details

thalf.eff islog(2)*mrt.

Value

the numeric value of the effective half-life

Determine time at or above a set value

Description

Interpolation is performed aligning withPKNCA.options("auc.method").Extrapolation outside of the measured times is not yet implemented. Themethod may be changed by giving a namedmethod argument, as well.

Usage

pk.calc.time_above(conc, time, conc_above, ..., options = list(), check = TRUE)

Arguments

Details

For'lin up/log down', ifclast is aboveconc_above and there areconcentrations BLQ after that, linear down is used to extrapolate to the BLQconcentration (equivalent to AUCall).

Value

the time above the given concentration

Determine the observed lag time (time before the firstconcentration above the limit of quantification or above the firstconcentration in the interval)

Description

Determine the observed lag time (time before the firstconcentration above the limit of quantification or above the firstconcentration in the interval)

Usage

pk.calc.tlag(conc, time)

Arguments

Value

The time associated with the first increasing concentration

Determine time of last observed concentration above the limit ofquantification.

Description

NA will be returned if allconc areNA or 0.

Usage

pk.calc.tlast(conc, time, check = TRUE)pk.calc.tfirst(conc, time, check = TRUE)

Arguments

Value

The time of the last observed concentration measurement

Functions

• pk.calc.tfirst(): Determine the first concentration abovethe limit of quantification.

Determine time of maximum observed PK concentration

Description

Input restrictions are:

1. theconc andtime must be the same length,

2. thetime may have no NAs,

NA will be returned if:

1. the length ofconc andtime is 0

2. allconc is 0 orNA

Usage

pk.calc.tmax(conc, time, options = list(), first.tmax = NULL, check = TRUE)

Arguments

Value

The time of the maximum concentration

Examples

conc_data <- Theoph[Theoph$Subject == 1,]pk.calc.tmax(conc = conc_data$conc, time = conc_data$Time)

Extract the dose used for calculations

Description

Extract the dose used for calculations

Usage

pk.calc.totdose(dose)

Arguments

Value

The total dose for an interval

Calculate the steady-state volume of distribution (Vss)

Description

Calculate the steady-state volume of distribution (Vss)

Usage

pk.calc.vss(cl, mrt)

Arguments

Details

vss iscl*mrt.

Value

the volume of distribution at steady-state

Calculate the terminal volume of distribution (Vz)

Description

Calculate the terminal volume of distribution (Vz)

Usage

pk.calc.vz(cl, lambda.z)

Arguments

Details

vz iscl/lambda.z.

Compute NCA parameters for each interval for each subject.

Description

Thepk.nca function computes the NCA parameters from aPKNCAdata object.All options for the calculation and input data are set in prior functions(PKNCAconc,PKNCAdose, andPKNCAdata). Options for calculations areset either inPKNCAdata or with the current default options inPKNCA.options.

Usage

pk.nca(data, verbose = FALSE)

Arguments

Details

When performing calculations, all time results are relative to the start ofthe interval. For example, if an interval starts at 168 hours, ends at 192hours, and and the maximum concentration is at 169 hours,tmax=169-168=1.

Value

APKNCAresults object.

See Also

PKNCAdata(),PKNCA.options(),summary.PKNCAresults(),as.data.frame.PKNCAresults(),exclude()

Compute all PK parameters for a single concentration-time data set

Description

For one subject/time range, compute all available PK parameters. All theinternal options should be set byPKNCA.options() prior to running. Theonly part that changes with a call to this function is theconcentrationandtime.

Usage

pk.nca.interval(  conc,  time,  volume,  duration.conc,  dose,  time.dose,  duration.dose,  route,  conc.group = NULL,  time.group = NULL,  volume.group = NULL,  duration.conc.group = NULL,  dose.group = NULL,  time.dose.group = NULL,  duration.dose.group = NULL,  route.group = NULL,  impute_method = NA_character_,  include_half.life = NULL,  exclude_half.life = NULL,  subject,  sparse,  interval,  options = list())

Arguments

Value

A data frame with the start and end time along with all PKparameters for theinterval

See Also

check.interval.specification()

Compute NCA for multiple intervals

Description

Compute NCA for multiple intervals

Usage

pk.nca.intervals(  data_conc,  data_dose,  data_intervals,  sparse,  options,  impute,  verbose = FALSE)

Arguments

Value

A data.frame with all NCA results

Compute the time to steady-state (tss)

Description

Compute the time to steady-state (tss)

Usage

pk.tss(..., type = c("monoexponential", "stepwise.linear"), check = TRUE)

Arguments

Value

A data frame with columns as defined frompk.tss.monoexponentialand/orpk.tss.stepwise.linear.

See Also

Other Time to steady-state calculations:pk.tss.monoexponential(),pk.tss.stepwise.linear()

Clean up the time to steady-state parameters and return a data frame for useby the tss calculators.

Description

Clean up the time to steady-state parameters and return a data frame for useby the tss calculators.

Usage

pk.tss.data.prep(  conc,  time,  subject,  treatment,  subject.dosing,  time.dosing,  options = list(),  conc.blq = NULL,  conc.na = NULL,  check = TRUE,  ...)

Arguments

Value

a data frame with columns forconcentration,time,subject,andtreatment.

Compute the time to steady state using nonlinear, mixed-effects modeling oftrough concentrations.

Description

Trough concentrations are selected as concentrations at the time of dosing.An exponential curve is then fit through the data with a different magnitudeby treatment (as a factor) and a random steady-state concentration and timeto stead-state by subject (seerandom.effects argument).

Usage

pk.tss.monoexponential(  ...,  tss.fraction = 0.9,  output = c("population", "popind", "individual", "single"),  check = TRUE,  verbose = FALSE)

Arguments

Value

A scalar float for the first time when steady-state is achieved orNA if it is not observed.

References

Maganti, L., Panebianco, D.L. & Maes, A.L. Evaluation of Methods forEstimating Time to Steady State with Examples from Phase 1 Studies. AAPS J10, 141–147 (2008). https://doi.org/10.1208/s12248-008-9014-y

See Also

Other Time to steady-state calculations:pk.tss(),pk.tss.stepwise.linear()

A helper function to estimate individual and single outputs formonoexponential time to steady-state.

Description

This function is not intended to be called directly. Please usepk.tss.monoexponential.

Usage

pk.tss.monoexponential.individual(  data,  output = c("individual", "single"),  verbose = FALSE)

Arguments

Details

If no model converges, then thetss.monoexponential.single and/ortss.monoexponential.individual column will be set to NA.

Value

A data frame with either one row (ifpopulation output isprovided) or one row per subject (ifpopind is provided). The columnswill be namedtss.monoexponential.population and/ortss.monoexponential.popind.

A helper function to estimate population and popind outputs formonoexponential time to steady-state.

Description

This function is not intended to be called directly. Please usepk.tss.monoexponential.

Usage

pk.tss.monoexponential.population(  data,  output = c("population", "popind"),  verbose = FALSE)

Arguments

Details

If no model converges, then thetss.monoexponential.population column willbe set to NA. If the best model does not include a random effect for subjecton Tss then thetss.monoexponential.popind column of the output will be setto NA.

Value

A data frame with either one row (ifpopulation output isprovided) or one row per subject (ifpopind is provided). The columnswill be namedtss.monoexponential.population and/ortss.monoexponential.popind.

Compute the time to steady state using stepwise test of linear trend

Description

A linear slope is fit through the data to find when it becomesnon-significant. Note that this is less preferred than thepk.tss.monoexponential due to the fact that with more time or more subjectsthe performance of the test changes (see reference).

Usage

pk.tss.stepwise.linear(  ...,  min.points = 3,  level = 0.95,  verbose = FALSE,  check = TRUE)

Arguments

Details

The model is fit with a different magnitude by treatment (as a factor, ifgiven) and a random slope by subject (if given). A minimum ofmin.pointsis required to fit the model.

Value

A scalar float for the first time when steady-state is achieved orNA if it is not observed.

References

Maganti L, Panebianco DL, Maes AL. Evaluation of Methods forEstimating Time to Steady State with Examples from Phase 1 Studies. AAPSJournal 10(1):141-7. doi:10.1208/s12248-008-9014-y

See Also

Other Time to steady-state calculations:pk.tss(),pk.tss.monoexponential()

Convert the grouping info and list of results for each group into a resultsdata.frame

Description

Convert the grouping info and list of results for each group into a resultsdata.frame

Usage

pk_nca_result_to_df(group_info, result)

Arguments

Value

A data.frame with group_info and result combined, warnings filteredout, and results unnested.

Find NCA parameters with a given unit type

Description

Find NCA parameters with a given unit type

Usage

pknca_find_units_param(unit_type)

Arguments

Value

A character vector of parameters with a given unit type

Perform unit conversion (if possible) on PKNCA results

Description

Perform unit conversion (if possible) on PKNCA results

Usage

pknca_unit_conversion(result, units, allow_partial_missing_units = FALSE)

Arguments

Value

The result table with units converted

Add parentheses to a unit value, if needed

Description

Add parentheses to a unit value, if needed

Usage

pknca_units_add_paren(unit)

Arguments

Value

The unit with parentheses around it, if needed

Create a unit assignment and conversion table

Description

This data.frame is typically used for theunits argument forPKNCAdata().If a unit is not given, then all of the units derived from that unit will beNA.

Usage

pknca_units_table(  concu,  doseu,  amountu,  timeu,  concu_pref = NULL,  doseu_pref = NULL,  amountu_pref = NULL,  timeu_pref = NULL,  conversions = data.frame())

Arguments

Value

A unit conversion table with columns for "PPTESTCD" and "PPORRESU"ifconversions is not given, and adding "PPSTRESU" and"conversion_factor" ifconversions is given.

See Also

Theunits argument forPKNCAdata()

Examples

pknca_units_table() # only parameters that are unitlesspknca_units_table(  concu="ng/mL", doseu="mg/kg", amountu="mg", timeu="hr")pknca_units_table(  concu="ng/mL", doseu="mg/kg", amountu="mg", timeu="hr",  # Convert clearance and volume units to more understandable units with  # automatic unit conversion  conversions=data.frame(    PPORRESU=c("(mg/kg)/(hr*ng/mL)", "(mg/kg)/(ng/mL)"),    PPSTRESU=c("mL/hr/kg", "mL/kg")  ))pknca_units_table(  concu="mg/L", doseu="mg/kg", amountu="mg", timeu="hr",  # Convert clearance and volume units to molar units (assuming  conversions=data.frame(    PPORRESU=c("mg/L", "(mg/kg)/(hr*ng/mL)", "(mg/kg)/(ng/mL)"),    PPSTRESU=c("mmol/L", "mL/hr/kg", "mL/kg"),    # Manual conversion of concentration units from ng/mL to mmol/L (assuming    # a molecular weight of 138.121 g/mol)    conversion_factor=c(1/138.121, NA, NA)  ))# This will make all time-related parameters use "day" even though the# original units are "hr"pknca_units_table(  concu = "ng/mL", doseu = "mg/kg", timeu = "hr", amountu = "mg",  timeu_pref = "day")

Print and/or summarize a PKNCAconc or PKNCAdose object.

Description

Print and/or summarize a PKNCAconc or PKNCAdose object.

Usage

## S3 method for class 'PKNCAconc'print(x, n = 6, summarize = FALSE, ...)## S3 method for class 'PKNCAconc'summary(object, n = 0, summarize = TRUE, ...)## S3 method for class 'PKNCAdose'print(x, n = 6, summarize = FALSE, ...)## S3 method for class 'PKNCAdose'summary(object, n = 0, summarize = TRUE, ...)

Arguments

Print a PKNCAdata object

Description

Print a PKNCAdata object

Usage

## S3 method for class 'PKNCAdata'print(x, ...)

Arguments

Print the summary of a provenance object

Description

Print the summary of a provenance object

Usage

## S3 method for class 'provenance'print(x, ...)

Arguments

Value

invisible text of the printed information

Print the results summary

Description

Print the results summary

Usage

## S3 method for class 'summary_PKNCAresults'print(x, ...)

Arguments

Value

x invisibly

See Also

summary.PKNCAresults()

Objects exported from other packages

Description

These objects are imported from other packages. Follow the linksbelow to see their documentation.

dplyr

filter,full_join,group_by,inner_join,left_join,mutate,right_join,ungroup

nlme

getGroups

Round a value to a defined number of digits printing out trailing zeros, ifapplicable.

Description

Round a value to a defined number of digits printing out trailing zeros, ifapplicable.

Usage

roundString(x, digits = 0, sci_range = Inf, sci_sep = "e", si_range)

Arguments

Details

Values that are not standard numbers likeInf,NA, andNaN are returned as"Inf","NA", andNaN.

Value

A string with the value

See Also

round(),signifString()

During the summarization of PKNCAresults, do the rounding of valuesbased on the instructions given.

Description

During the summarization of PKNCAresults, do the rounding of valuesbased on the instructions given.

Usage

roundingSummarize(x, name)

Arguments

Value

A string of the rounded value

Add an attribute to an object where the attribute is added as a nameto the names of the object.

Description

Add an attribute to an object where the attribute is added as a nameto the names of the object.

Usage

setAttributeColumn(  object,  attr_name,  col_or_value,  col_name,  default_value,  stop_if_default,  warn_if_default,  message_if_default)

Arguments

Value

The object with the attribute column added to the data.

See Also

getAttributeColumn()

Set the duration of dosing or measurement

Description

Set the duration of dosing or measurement

Usage

## S3 method for class 'PKNCAconc'setDuration(object, duration, ...)setDuration(object, ...)## S3 method for class 'PKNCAdose'setDuration(object, duration, rate, dose, ...)

Arguments

Value

The object with duration set

Set the exclude parameter on an object

Description

This function adds the exclude column to an object. To change theexclude value, use theexclude() function.

Usage

setExcludeColumn(object, exclude = NULL, dataname = "data")

Arguments

Value

The object with an exclude column and attribute

Set the dosing route

Description

Set the dosing route

Usage

setRoute(object, ...)## S3 method for class 'PKNCAdose'setRoute(object, route, ...)

Arguments

Value

The object with an updated route

Set Intervals

Description

Takes in two objects, the PKNCAdata object and the proposed intervals.It will then check that the intervals are valid, given the data object.If the intervals are valid, it will set them in the object.It will return the data object with the intervals set.

Usage

set_intervals(data, intervals)

Arguments

Value

The data object with the intervals set.

Round a value to a defined number of significant digits printing out trailingzeros, if applicable.

Description

Round a value to a defined number of significant digits printing out trailingzeros, if applicable.

Usage

signifString(x, ...)## S3 method for class 'data.frame'signifString(x, ...)## Default S3 method:signifString(x, digits = 6, sci_range = 6, sci_sep = "e", si_range, ...)

Arguments

Details

Values that are not standard numbers likeInf,NA, andNaN are returned as"Inf","NA", andNaN.

Value

A string with the value

See Also

signif(),roundString()

Sort the interval columns by dependencies.

Description

Columns are always to the right of columns that they depend on.

Usage

## S3 method for class 'interval.cols'sort()

Calculate the weight for sparse AUC calculation with the linear-trapezoidalrule

Description

The weight is used as thew_i parameter inpk.calc.sparse_auc()

Usage

sparse_auc_weight_linear(sparse_pk)

Arguments

Details

w_i = \frac{\delta_{time,i-1,i} + \delta_{time,i,i+1}}{2}

\delta_{time,i,i+1} = t_{i+1} - t_i

Where:

w_i

is the weight at time i

\delta_{time,i-1,i} and\delta_{time,i,i+1}

are the changes between time i-1 and i or i and i+1 (zero outside of the time range)

t_i

is the time at time i

Value

A numeric vector of weights for sparse AUC calculations the samelength assparse_pk

See Also

Other Sparse Methods:as_sparse_pk(),pk.calc.sparse_auc(),sparse_mean()

Calculate the mean concentration at all time points for use in sparse NCAcalculations

Description

Choices for the method of calculation (the argumentsparse_mean_method)are:

Usage

sparse_mean(  sparse_pk,  sparse_mean_method = c("arithmetic mean, <=50% BLQ", "arithmetic mean"))

Arguments

Details

"arithmetic mean"

Arithmetic mean (ignoring number of BLQ samples)

"arithmetic mean, <=50% BLQ"

If >= 50% of the measurements are BLQ, zero. Otherwise, the arithmetic mean of all samples (including the BLQ as zero).

Value

A vector the same length assparse_pk with the mean concentrationat each of those times.

See Also

Other Sparse Methods:as_sparse_pk(),pk.calc.sparse_auc(),sparse_auc_weight_linear()

Set or get a sparse_pk object attribute

Description

Set or get a sparse_pk object attribute

Usage

sparse_pk_attribute(sparse_pk, ...)

Arguments

Value

Either the attribute value or an updatedsparse_pk object

Extract the mean concentration-time profile as a data.frame

Description

Extract the mean concentration-time profile as a data.frame

Usage

sparse_to_dense_pk(sparse_pk)

Arguments

Value

A data.frame with names of "conc" and "time"

Summarize a PKNCAdata object showing important details about theconcentration, dosing, and interval information.

Description

Summarize a PKNCAdata object showing important details about theconcentration, dosing, and interval information.

Usage

## S3 method for class 'PKNCAdata'summary(object, ...)

Arguments

Summarize PKNCA results

Description

Summarize PKNCA results

Usage

## S3 method for class 'PKNCAresults'summary(  object,  ...,  drop_group = object$data$conc$columns$subject,  drop_param = character(),  summarize_n = NA,  not_requested = ".",  not_calculated = "NC",  drop.group = deprecated(),  summarize.n.per.group = deprecated(),  not.requested.string = deprecated(),  not.calculated.string = deprecated(),  pretty_names = NULL)

Arguments

Details

Excluded results will not be included in the summary.

Value

A data frame of NCA parameter results summarized according to thesummarization settings.

See Also

PKNCA.set.summary(),print.summary_PKNCAresults()

Examples

conc_obj <- PKNCAconc(as.data.frame(datasets::Theoph), conc ~ Time | Subject)d_dose <-  unique(datasets::Theoph[    datasets::Theoph$Time == 0,    c("Dose", "Time", "Subject")  ])dose_obj <- PKNCAdose(d_dose, Dose ~ Time | Subject)data_obj_automatic <- PKNCAdata(conc_obj, dose_obj)results_obj_automatic <- pk.nca(data_obj_automatic)# To get standard results run summarysummary(results_obj_automatic)# To enable numeric conversion and extraction, do not give a spread function# and subsequently run as.numeric on the result columns.PKNCA.set.summary(  name = c("auclast", "cmax", "half.life", "aucinf.obs"),  point = business.geomean,  description = "geometric mean")PKNCA.set.summary(  name = c("tmax"),  point = business.median,  description = "median")summary(results_obj_automatic, not_requested = "NA")

Compute noncompartmental superposition for repeated dosing

Description

Compute noncompartmental superposition for repeated dosing

Usage

superposition(conc, ...)## S3 method for class 'PKNCAconc'superposition(conc, ...)## S3 method for class 'numeric'superposition(  conc,  time,  dose.input = NULL,  tau,  dose.times = 0,  dose.amount,  n.tau = Inf,  options = list(),  lambda.z,  clast.pred = FALSE,  tlast,  additional.times = numeric(),  check.blq = TRUE,  method = NULL,  auc.type = "AUCinf",  steady.state.tol = 0.001,  ...)

Arguments

Details

The returned superposition times will include all of the followingtimes: 0 (zero),dose.times,⁠time modulo tau⁠ (shiftingtime for eachdose time as well),additional.times, andtau.

Value

A data frame with columns named "conc" and "time".

See Also

interp.extrap.conc()

Times relative to an event (typically dosing)

Description

Times relative to an event (typically dosing)

Usage

time_calc(time_event, time_obs, units = NULL)

Arguments

Value

A data.frame with columns for:

event_number_before

The index oftime_event that is the last one beforetime_obs orNA if none are before.

event_number_after

The index oftime_event that is the first one aftertime_obs orNA if none are after.

time_before

The minimum time that the currenttime_obs is before atime_event, 0 if at least onetime_obs == time_event.

time_after

The minimum time that the currenttime_obs is after atime_event, 0 if at least onetime_obs == time_event.

time_after_first

The time after the first event (may be negative or positive).

time_after andtime_before are calculated if they are at the same time asa dose, they equal zero, and otherwise, they are calculated relative to thedose number in the⁠event_number_*⁠ columns.

A helper function to generate the formula and starting values for theparameters in monoexponential models.

Description

A helper function to generate the formula and starting values for theparameters in monoexponential models.

Usage

tss.monoexponential.generate.formula(data)

Arguments

Value

a list with elements for each of the variables

Calculate the variance for the AUC of sparsely sampled PK

Description

Equation 7.vii in Nedelman and Jia, 1998 is used for this calculation:

Usage

var_sparse_auc(sparse_pk)

Arguments

Details

var\left(\hat{AUC}\right) = \sum\limits_{i=0}^m\left(\frac{w_i^2 s_i^2}{r_i}\right) + 2\sum\limits_{i<j}\left(\frac{w_i w_j r_{ij} s_{ij}}{r_i r_j}\right)

The degrees of freedom are calculated as described in equation 6 of the samepaper.

References

Nedelman JR, Jia X. An extension of Satterthwaite’s approximation applied topharmacokinetics. Journal of Biopharmaceutical Statistics. 1998;8(2):317-328.doi:10.1080/10543409808835241