Package 'qpNCA' reference manual

Title:	Noncompartmental Pharmacokinetic Analysis by qPharmetra
Description:	Computes noncompartmental pharmacokinetic parameters for drug concentration profiles. For each profile, data imputations and adjustments are made as necessary and basic parameters are estimated. Supports single dose, multi-dose, and multi-subject data. Supports steady-state calculations and various routes of drug administration. See ?qpNCA and vignettes. Methodology follows Rowland and Tozer (2011, ISBN:978-0-683-07404-8), Gabrielsson and Weiner (1997, ISBN:978-91-9765-100-4), and Gibaldi and Perrier (1982, ISBN:978-0824710422).
Authors:	Jan Huisman [aut, cre], Koen Jolling [ctb], Krina Mehta [ctb], Tim Bergsma [ctb]
Maintainer:	Jan Huisman <[email protected]>
License:	GPL-3
Version:	1.1.6
Built:	2024-11-13 03:59:18 UTC
Source:	https://github.com/cran/qpNCA

Calculate Cmax and Tmax

Description

Calculates Cmax and Tmax from raw data for each PK curve defined using by.

Usage

calc.ctmax(x, by = character(0), timevar = "time", depvar = "dv")
calc.ctmax(x, by = character(0), timevar = "time", depvar = "dv")

Arguments

`x`	data.frame
`by`	column names in x indicating grouping variables
`timevar`	variable name containing the actual sampling time after dose
`depvar`	variable name containing the dependent variable (e.g., concentration)

Details

Input dataset can contain all uncorrected data, including LOQ; estimate first occurence of maximum concentration for each PK curve; if all concentrations are NA, sets Cmax and Tmax also to NA.

Value

A dataset with estimates for the Cmax (maximum concentration) and Tmax (time of first occurence of cmax) parameters: one observation per subject

Examples


example(est.thalf)
ctmax <- x %>% calc.ctmax(by = 'subject')
ctmax %>% head

example(est.thalf)
ctmax <- x %>% calc.ctmax(by = 'subject')
ctmax %>% head

Calculate NCA Parameters

Description

Calculates PK parameters for which half-life is not needed in the calculation for each PK curve defined using by.

Usage

calc.par(
  x,
  by = character(0),
  tau = NA,
  tstart = NA,
  tend = NA,
  teval = NA,
  route = "EV",
  method = 1
)
calc.par(
  x,
  by = character(0),
  tau = NA,
  tstart = NA,
  tend = NA,
  teval = NA,
  route = "EV",
  method = 1
)

Arguments

`x`	contains all data after time/concentration deviation corrections obtained from `correct.time` and `correct.conc`
`by`	column names in x indicating grouping variables
`tau`	dosing interval (for multiple dosing); NA (default) for if single dose; x$tau overrides
`tstart`	start time of partial AUC (start>0); NA (default) if not requested; x$tstart overrides
`tend`	end time of partial AUC; NA (default) if not requested; x$tend overrides
`teval`	user selected AUC interval; NA (default) if not requested; x$teval overrides
`route`	route of drug administration ("EV","IVB","IVI"); x$route overrides
`method`	method for trapezoidal rule 1: linear up - linear down 2: linear up - logarithmic down 3: linear before first Tmax, logarithmic after first Tmax

Value

A dataset with estimates for the following parameters, one observation per subject:

Parameter	Description
t0.ok	flags if t=0 concentration could be corrected/imputes. If not, no AUCs starting at t=0 are calculated
tlast.ok	flags if there is at least one measurable concentration. If not, no AUClast can be calculated
tlast	time of last sample with measurable concentration
clast.obs	observed concentration at tlast
aucall	auc calculated over all observations, including values below LOQ (which are set to 0)
auclast	auc calculated using all observations up to and including the last measurable concentration (clast.obs at tlast)
aumcall	aumc calculated over all observations, including values below LOQ (which are set to 0)
aumclast	aumc calculated using all observations up to and including the last measurable concentration (clast.obs at tlast)
tau	the dosing interval (if specified)
calc.tau	flags if AUCtau could be calculated
auctau	auc calculated over the dosing interval, only calculated if tau is specified
aumctau	aumc calculated over the dosing interval, only calculated if tau is specified
teval	user selected AUC interval starting at t=0 (if specified)
calc.teval	flags if AUCteval could be calculated
aucxx	auc calculated from t=0 up to/including teval, only calculated if teval is specified (xx is substituted by teval)
calc.part	flags if AUCpart could be calculated
tstart	start time of partial AUC (if specified)
tend	end time of partial AUC (if specified)
aucx_y	partial auc from time=x up to/including time=y, where x>0, only calculated if tstart and tend are specified
c0	back-extrapolated concentration at t=0 for IV bolus administration
area.back.extr	area back-extrapolated to 0

Examples


example(correct.conc)
par <- x %>% calc.par(by = 'subject')
par %>% head

example(correct.conc)
par <- x %>% calc.par(by = 'subject')
par %>% head

Calculate Lambda_z Parameters

Description

Calculates PK parameters that need lambda_z.

Usage

calc.par.th(
  x,
  by = character(0),
  th = th,
  covariates = NA,
  dose = "dose",
  factor = 1,
  reg = "SD",
  ss = "N",
  route = "EV"
)
calc.par.th(
  x,
  by = character(0),
  th = th,
  covariates = NA,
  dose = "dose",
  factor = 1,
  reg = "SD",
  ss = "N",
  route = "EV"
)

Arguments

`x`	result parameter dataset from `calc.par`
`by`	column names in x indicating grouping variables
`th`	result dataset from `est.thalf`
`covariates`	covariates dataset (containing at least dose for CL calculation); defaults to unique combinations of `by` and `dose` evaluated on `x`; can be character name of csv file or local object
`dose`	variable containing the dose amount; default 'dose' set to 1 if not in `names(x)`
`factor`	conversion factor for CL and V calculation (e.g. dose in mg, conc in ng/mL, factor=1000); x$factor overrides
`reg`	regimen, "sd" or "md"; x$reg overrides
`ss`	is steady state reached (y/n); x$ss overrides
`route`	of drug administration ("EV","IVB","IVI"); x$route overrides

Value

A dataset containing all parameters calculated in est.thalf and calc.par
with estimates for the following parameters added, one observation per subject:

Parameter	Description
clast.pred	predicted concentration at tlast
aucinf.obs	aucinf based on observed concentration at tlast
aucinf.pred	aucinf based on predicted concentration at tlast
aumcinf.obs	area under the first moment curve extrapolated to infinity, based on observed concentration at tlast
aumcinf.pred	area under the first moment curve extrapolated to infinity, based on predicted concentration at tlast
cl.obs, cl.f.obs	clearance based on aucinf.obs, at steady state based on auctau
cl.pred, cl.f.pred	clearance based on aucinf.pred
cl.ss, cl.f.ss	clearance at steady state, based on auctau
mrt.obs	mean residence time based on aumcinf.obs and aucinf.obs
mrt.pred	mean residence time based on aumcinf.pred and aucinf.pred
vz.obs, vz.f.obs	distribution volume based on cl.f.obs, at steady state based on auctau
vz.pred, vz.f.pred	distribution based on cl.pred/cl.f.pred
vss.obs	steady-state volume based on cl.obs and mrt.obs
vss.pred	steady-state volume based on cl.pred and mrt.pred
pctextr.pred	percentage of AUC extrapolated to infinity, based on aucinf.pred
pctextr.obs	percentage of AUC extrapolated to infinity, based on aucinf.obs
pctback.pred	percentage of AUC extrapolated back to 0, based on aucinf.pred
pctback.obs	percentage of AUC extrapolated back to 0, based on aucinf.obs

Note: ctmax must be merged separately as those were calculated from uncorrected data.

Examples


example(calc.par) # creates par
# notice x includes (optional) loqrule, includeCmax, reg, method, route, ss
covs <- Theoph %>%
  select(subject = Subject, Wt, dose = Dose) %>%
  unique %>%
  mutate(dose = dose * Wt, subject=as.numeric(as.character(subject))) # see ?Theoph
y <- x %>% select(subject, reg, ss, loqrule) %>% unique
y %<>% mutate(factor = 1)
par %<>% left_join(y, by = 'subject')
par %<>% calc.par.th(by = 'subject', th = th, covariates = covs)
par %<>% left_join(ctmax, ., by = 'subject')
par %>% head
par %>% data.frame %>% head(2)

example(calc.par) # creates par
# notice x includes (optional) loqrule, includeCmax, reg, method, route, ss
covs <- Theoph %>%
  select(subject = Subject, Wt, dose = Dose) %>%
  unique %>%
  mutate(dose = dose * Wt, subject=as.numeric(as.character(subject))) # see ?Theoph
y <- x %>% select(subject, reg, ss, loqrule) %>% unique
y %<>% mutate(factor = 1)
par %<>% left_join(y, by = 'subject')
par %<>% calc.par.th(by = 'subject', th = th, covariates = covs)
par %<>% left_join(ctmax, ., by = 'subject')
par %>% head
par %>% data.frame %>% head(2)

Check qpNCA function arguments for validity

Description

Checks whether all function arguments are valid and entered column names are present in input data
See qpNCA for description of the arguments

Usage

check.input(
  x,
  by = NA,
  nomtimevar = NA,
  timevar = NA,
  depvar = NA,
  bloqvar = NA,
  loqvar = NA,
  loqrule = NA,
  includeCmax = NA,
  exclvar = NA,
  plotdir = NA,
  timelab = NA,
  deplab = NA,
  tau = NA,
  tstart = NA,
  tend = NA,
  teval = NA,
  covariates = NA,
  dose = NA,
  factor = NA,
  reg = NA,
  ss = NA,
  route = NA,
  method = NA
)
check.input(
  x,
  by = NA,
  nomtimevar = NA,
  timevar = NA,
  depvar = NA,
  bloqvar = NA,
  loqvar = NA,
  loqrule = NA,
  includeCmax = NA,
  exclvar = NA,
  plotdir = NA,
  timelab = NA,
  deplab = NA,
  tau = NA,
  tstart = NA,
  tend = NA,
  teval = NA,
  covariates = NA,
  dose = NA,
  factor = NA,
  reg = NA,
  ss = NA,
  route = NA,
  method = NA
)

Arguments

`x`	data.frame
`by`	column names in x indicating grouping variables
`nomtimevar`	variable name containing the nominal sampling time after dose
`timevar`	variable name containing the actual sampling time after dose
`depvar`	variable name containing the dependent variable (e.g., concentration)
`bloqvar`	variable name containing the BLOQ flag (0: no, 1: yes)
`loqvar`	variable name containing the LOQ value
`loqrule`	rule number to be applied to the LOQ values in the curve; x$loqrule overrides if provided
`includeCmax`	include Cmax in half-life estimation? (y/n); x$includeCmax overrides if provided
`exclvar`	variable name indicating points to be excluded in half-life estimation (these should have exclvar = 1)
`plotdir`	directory where regression plots (.PNG) will be saved; NA gives default location, NULL skips regression plots
`timelab`	label for time axis in regression plots
`deplab`	label for dependent variable axis in regression plots
`tau`	dosing interval (for multiple dosing); NA (default) if single dose; x$tau overrides
`tstart`	start time of partial AUC (start>0); NA (default) if not requested; x$tstart overrides
`tend`	end time of partial AUC; NA (default) if not requested; x$tend overrides
`teval`	user selected AUC interval (starting at t=0); NA (default) if not requested; x$teval overrides
`covariates`	covariates dataset; Must contain the dose variable
`dose`	variable containing the dose amount
`factor`	conversion factor for CL and V calculation (e.g. dose in mg, conc in ng/mL, factor=1000); x$factor overrides if provided
`reg`	regimen, "SD" or "MD"; x$reg overrides if provided
`ss`	is steady state reached (y/n); x$ss overrides if provided
`route`	route of drug administration ("EV","IVB", "IVI"); x$route overrides if provided
`method`	method for trapezoidal rule; x$method overrides if provided

Value

Check results

Correct Missing Concentration

Description

Corrects missing concentration at critical time points (e.g, predose, TAU, start and end of user selected AUC interval).

Usage

correct.conc(
  x,
  by = character(0),
  nomtimevar = "ntad",
  tau = NA,
  tstart = NA,
  tend = NA,
  teval = NA,
  th = NA,
  reg = "SD",
  ss = "N",
  route = "EV",
  method = 1
)
correct.conc(
  x,
  by = character(0),
  nomtimevar = "ntad",
  tau = NA,
  tstart = NA,
  tend = NA,
  teval = NA,
  th = NA,
  reg = "SD",
  ss = "N",
  route = "EV",
  method = 1
)

Arguments

`x`	input dataset name (after Time Deviation Correction Rules have been applied by `correct.time`)
`by`	column names in x indicating grouping variables
`nomtimevar`	variable name containing the nominal sampling time after dose
`tau`	dosing interval (for multiple dosing); NA (default) for if single dose; x$tau overrides
`tstart`	start time of partial AUC (start>0); NA (default) if not requested; x$tstart overrides
`tend`	end time of partial AUC; NA (default) if not requested; x$tend overrides
`teval`	user selected AUC interval; NA (default) if not requested; x$teval overrides
`th`	lamdba_z information for each curve; like output of `est.thalf`
`reg`	regimen, "sd" or "md"; x$reg overrides
`ss`	is steady state reached (y/n); x$ss overrides
`route`	route of drug administration ("EV","IVB","IVI"); x$route overrides
`method`	method for trapezoidal rule; x$method overrides 1: linear up - linear down 2: linear up - logarithmic down 3: linear before first Tmax, logarithmic after first Tmax

Details

Use interpolation if there is a measurable concentration BEFORE and AFTER the missing concentration
Use extrapolation if there is NO measurable concentration AFTER the missing concentration
Set missing concentration at predose to 0 (SD, non-endogenous) or value at t=TAU (steady state only)
Set missing concentration at t=TAU to value at t=0 (steady state only)

The following Concentration Deviation Correction Rules will be applied to critical time points (t=0, tau, tstart, tend, teval), if needed:

Rule	Regimen	Description	Applied to
SDC-1	sd	Set concentration to 0 (only non-endogenous compounds)	t=0
SDC-2	sd	impute missing concentration by interpolation	t=tau,tstart,tend,teval
SDC-3	sd	impute missing concentration by extrapolation	t=tau,tend,teval
SDC-4	sd (IVB)	impute missing concentration by back-extrapolation	t=0
MDC-1	md	impute missing concentration by existing conc at t=0 or t=tau (only if steady state has been reached)	t=0,tau
MDC-2	md	impute missing concentration by interpolation	t=tau,tstart,tend,teval
MDC-3	md	impute missing concentration by extrapolation	t=tau,tend,teval
MDC-4	md (IVB)	impute missing concentration by back-extrapolation	t=0

Value

a dataset with missing concentrations imputed. The following variables are added:

Variable	Description
crule.nr	correction rule number
crule.txt	text explaining what was altered
applies.to.conc	lists all critical time points to which the concentration correction rule applies

Examples


example(correct.time)
x %<>% mutate(ss = 'N', route = 'EV')
# route redefined for completeness
x %<>% correct.conc(by = 'subject') # ignoring th
x %>% head

example(correct.time)
x %<>% mutate(ss = 'N', route = 'EV')
# route redefined for completeness
x %<>% correct.conc(by = 'subject') # ignoring th
x %>% head

Impute Concentrations Below the Limit of Quantitation

Description

Imputes LOQ values according to the chosen LOQ substitution rule.

Usage

correct.loq(
  x,
  by = character(0),
  nomtimevar = "ntad",
  timevar = "time",
  depvar = "dv",
  bloqvar = "bloq",
  loqvar = "loq",
  loqrule = 1
)
correct.loq(
  x,
  by = character(0),
  nomtimevar = "ntad",
  timevar = "time",
  depvar = "dv",
  bloqvar = "bloq",
  loqvar = "loq",
  loqrule = 1
)

Arguments

`x`	input dataset name contains all uncorrected data, including LOQ
`by`	column names in x indicating grouping variables
`nomtimevar`	variable name containing the nominal sampling time after dose
`timevar`	variable name containing the actual sampling time after dose
`depvar`	variable name containing the dependent variable (e.g., concentration)
`bloqvar`	variable name containing the BLOQ flag (0: no, 1: yes)
`loqvar`	variable name containing the LOQ value
`loqrule`	rule number to be applied to the LOQ values in the curve. x$loqrule overrides if provided 1: 0 before first measurable concentration (FMC); NA after FMC 2: 0 before FMC; 0 after FMC 3: 0 before FMC; 0.5xLOQ for first consecutive LOQ after FMC, NA for other LOQ 4: 0 before FMC; 0.5xLOQ for first consecutive LOQ after FMC, 0 for other LOQ

Details

Imputations will be applied to the original depvar (no new concentration variable will be created).

Value

A dataset with imputed BLOQ concentrations using the chosen imputation rule

Examples


library(magrittr)
library(dplyr)
library(qpNCA)
x <- Theoph
ntad <- c(0,0.25,0.5,1,2,4,5,7,9,12,24)
for(i in 1:nrow(x)){
  time  <- x$Time[[i]]
  delta <- abs(ntad - time)
  best  <- min(delta)
  index <- match(best, delta)
  nom   <- ntad[[index]]
  x$ntad[[i]] <- nom
}
rm(list = c('time','delta','best','index','nom', 'i','ntad'))
x %<>% rename(time = Time, dv = conc)
x %<>% mutate(bloq = ifelse(dv==0,1,0), loq = 0.01, tad = time, loqrule = 1, 
              subject=as.numeric(Subject), ntad=as.numeric(ntad))
x %>% head
x %<>% correct.loq('subject')
x %>%  head

library(magrittr)
library(dplyr)
library(qpNCA)
x <- Theoph
ntad <- c(0,0.25,0.5,1,2,4,5,7,9,12,24)
for(i in 1:nrow(x)){
  time  <- x$Time[[i]]
  delta <- abs(ntad - time)
  best  <- min(delta)
  index <- match(best, delta)
  nom   <- ntad[[index]]
  x$ntad[[i]] <- nom
}
rm(list = c('time','delta','best','index','nom', 'i','ntad'))
x %<>% rename(time = Time, dv = conc)
x %<>% mutate(bloq = ifelse(dv==0,1,0), loq = 0.01, tad = time, loqrule = 1, 
              subject=as.numeric(Subject), ntad=as.numeric(ntad))
x %>% head
x %<>% correct.loq('subject')
x %>%  head

Correct Concentrations for Time Deviations

Description

Corrects concentrations at critical, but deviating time points (e.g, predose, TAU, start and end of user selected AUC interval), and adds missing records at these critical time points.

Usage

correct.time(
  x,
  by = character(0),
  nomtimevar = "ntad",
  timevar = "time",
  depvar = "dv",
  tau = NA,
  tstart = NA,
  tend = NA,
  teval = NA,
  th = NA,
  reg = "SD",
  method = 1
)
correct.time(
  x,
  by = character(0),
  nomtimevar = "ntad",
  timevar = "time",
  depvar = "dv",
  tau = NA,
  tstart = NA,
  tend = NA,
  teval = NA,
  th = NA,
  reg = "SD",
  method = 1
)

Arguments

`x`	input dataset name (after LOQ values have been imputed by `correct.loq`)
`by`	column names in x indicating grouping variables
`nomtimevar`	variable name containing the nominal sampling time after dose
`timevar`	variable name containing the actual sampling time after dose
`depvar`	variable name containing the dependent variable (e.g., concentration)
`tau`	dosing interval (for multiple dosing); NA (default) if single dose; x$tau overrides
`tstart`	start time of partial AUC (start>0); NA (default) if not requested; x$tstart overrides
`tend`	end time of partial AUC; NA (default) if not requested; x$tend overrides
`teval`	user selected AUC interval, starting at t=0; NA (default) if not requested; x$teval overrides
`th`	lamdba_z information for each curve; like output of `est.thalf`
`reg`	regimen, "sd" or "md"; x$reg overrides
`method`	method for trapezoidal rule; x$method overrides if provided 1: linear up - linear down 2: linear up - logarithmic down 3: linear before first Tmax, logarithmic after first Tmax

Details

Records with missing NOMINAL time will be removed and this must be corrected before the function is called
If a record at the critical time point is missing, add it and set time to nominal time and set dv conc to NA
Use interpolation if there is a measurable concentration AFTER the nominal time point (i.e. sample is taken too late)
Use extrapolation if there is NO measurable concentration AFTER the nominal time point (i.e. sample is taken too early)
Set deviating time at predose after single dose to 0
Original time and conc will be kept in original variables.

The following Time Deviation Correction Rules will be applied to critical time points (t = 0, tau, tstart, tend, teval), if needed:

Rule	Regimen	Description	Applied to
SDT-1	sd	Set actual time to 0	t = 0
SDT-2	sd	Correct concentration at deviating time by interpolation	t = tau,tstart,tend,teval
SDT-3	sd	Correct concentration at deviating time by extrapolation	t = tau,tend,teval
MDT-1	md	If predose sample taken after dosing, set actual time to 0 and conc to NA	t = 0
MDT-2	md	Correct concentration at deviating time by interpolation (too late)	t = tau,tstart,tend,teval
MDT-3	md	Correct concentration at deviating time by extrapolation (too early)	t = 0,tau,tend,teval
MDT-3a	md	Set actual time to zero if concentration is BLOQ (too early)	t = 0

Value

a dataset with time deviation corrections applied (timevar and depvar adapted). The following variables are added:

Variable	Description
create.nr	is a missing record created?
create.txt	explanation of what is created
trule.nr	correction rule number
trule.txt	text explaining what was altered
applies.to.time	lists all critical time points to which the time deviation rule applies
time.tau, conc.tau	time and conc, corrected for AUCtau calculation
time.teval, conc.teval	time and conc, corrected for AUCteval calculation (AUC0-teval)
time.part, conc.part	time and conc, corrected for partial AUC calculation (AUCstart-end, start>0)
time.lastall, conc.lastall	time and conc, corrected for AUClast and AUCall calculation
t0.flag, tau.flag, tstart.flag, tend.flag, teval.flag	flags for what timepoint the correction was needed

The following are preserved if present in x: tau, tstart, tend, teval, reg, ss, route, method.

Examples


example(calc.ctmax)
x %<>% mutate(reg = 'SD', method = 1, route = 'EV')
# route not used yet, but still preserved
x %<>% correct.time(by = 'subject', th = th)
x %>% head

example(calc.ctmax)
x %<>% mutate(reg = 'SD', method = 1, route = 'EV')
# route not used yet, but still preserved
x %<>% correct.time(by = 'subject', th = th)
x %>% head

Calculate Lambda_z and Elimination Half-life

Description

Calculates lambda_z and thalf for each PK curve identified using by.

Usage

est.thalf(
  x,
  by = character(0),
  timevar = "time",
  depvar = "dv",
  includeCmax = "Y",
  exclvar = NA
)
est.thalf(
  x,
  by = character(0),
  timevar = "time",
  depvar = "dv",
  includeCmax = "Y",
  exclvar = NA
)

Arguments

`x`	a dataset
`by`	column names in x indicating grouping variables
`timevar`	variable name containing the actual sampling time after dose
`depvar`	variable name containing the dependent variable (e.g., concentration)
`includeCmax`	include results of regression including Cmax in selection? (y/n); x$includeCmax overrides if provided
`exclvar`	a variable name containing information about points to be excluded (these should have exclvar = 1)

Details

The function starts with the last three sample points and performs log-linear regression on it. It then adds one sampling point at a time (including and ending at tmax) and performs the regression again. The results of the regression with the highest adjusted R-squared are returned.

Visual outliers can be excluded from the regression analysis.

Value

a dataset with estimates for each regression analysis in one observation. The following parameters are available.

no.points number of data points used in the regression analysis
intercept estimated intercept
lambda_z -1*estimated slope
r.squared square of the correlation coefficient
adj.r.squared adjusted square of the correlation coefficient
thalf elimination half-life
start_th time of first sample included in the thalf estimation
end_th time of last sample included in the thalf estimation
includeCmax include results of regression including Cmax in selection? (y/n)
points_excluded are time points excluded from the half-life estimation? (y/n)

Examples


example(correct.loq)
x %<>% mutate(includeCmax = 'Y')
th <- x %>% est.thalf(by='subject',exclvar=)
th %>%  head

example(correct.loq)
x %<>% mutate(includeCmax = 'Y')
th <- x %>% est.thalf(by='subject',exclvar=)
th %>%  head

Create File Name for Regression Plots

Description

Creates file name for regression plots (*.png) from by-variables in plot_reg function

Usage

filenamefun(x, by)
filenamefun(x, by)

Arguments

`x`	data.frame
`by`	column names in x indicating grouping variables

Value

character

Interpolate Concentrations

Description

Interpolates concentrations. Used by correct.xx functions to interpolate concentrations. Uses linear interpolation unless method is 2 (log down), c1 > c2, and both concentrations are non-zero.

Usage

interpol(c1 = NA, c2 = NA, t1 = NA, t2 = NA, t3 = NA, method = 1)
interpol(c1 = NA, c2 = NA, t1 = NA, t2 = NA, t3 = NA, method = 1)

Arguments

`c1`	concentration 1 lagconc
`c2`	concentration 2 leadconc
`t1`	time 1 tiem where conc should be calculated
`t2`	time 2 lagtime
`t3`	time 3 leadtime
`method`	calculation method (1, 2, or 3)

Estimate Lagging and Leading Times and Concentrations

Description

Estimates lagging and leading times and concentrations. Used by correct.xx functions to estimate lagging and leading timepoints and concentrations for each timepoint.

Usage

lag_lead(
  x,
  nomtimevar1 = NA,
  depvar1 = NA,
  timevar1 = NA,
  lagc = NA,
  lagt = NA,
  leadc = NA,
  leadt = NA,
  ...
)
lag_lead(
  x,
  nomtimevar1 = NA,
  depvar1 = NA,
  timevar1 = NA,
  lagc = NA,
  lagt = NA,
  leadc = NA,
  leadt = NA,
  ...
)

Arguments

`x`	data.frame
`nomtimevar1`	column name in x indicating nominal time after dose
`depvar1`	column name in x indicating concentration
`timevar1`	column name in x indicating actual time after dose
`lagc`	concentration at previous sampling time
`lagt`	previous sampling time
`leadc`	concentration at next sampling time
`leadt`	next sampling time
`...`	ignored

Value

data.frame

Plot Regression Curves

Description

Plots regression curves for each set of records defined using by. A log-linear plot will be made for each curve.

Usage

plot_reg(
  x,
  by = character(0),
  th = NA,
  bloqvar = "bloq",
  timevar = "tad",
  depvar = "dv",
  timelab = "timevar",
  deplab = "depvar",
  exclvar = NA,
  plotdir = NA,
  ...
)
plot_reg(
  x,
  by = character(0),
  th = NA,
  bloqvar = "bloq",
  timevar = "tad",
  depvar = "dv",
  timelab = "timevar",
  deplab = "depvar",
  exclvar = NA,
  plotdir = NA,
  ...
)

Arguments

`x`	input dataset name
`by`	column names in x indicating grouping variables
`th`	file name of file with half-life estimation information for each curve
`bloqvar`	variable name containing the BLOQ flag (0: no, 1: yes)
`timevar`	variable name containing the actual sampling time after dose
`depvar`	variable name containing the dependent variable (e.g., concentration)
`timelab`	X-axis label (default: "timevar")
`deplab`	Y-axis label (default: "depvar")
`exclvar`	variable name containing information about points to be excluded (these should have exclvar = 1)
`plotdir`	directory where individual plot files will be saved
`...`	ignored

Details

If elimination half-life was estimated for that curve, the following will be indicated in the plot:

Cmax (Yellow, even if no half-life was estimated)
points used in regression and resulting regression line (green)
points excluded from regression (red crossed)
estimate of elimination half-life and adjusted R-squared

Input dataset:

uncorrected dataset, used for half-life estimation
dataset containing results of the half-life estimation

Value

(invisible) plotdir. If the attribute 'plotdir' is empty, plots will be generated in standard output, otherwise plots will be saved as PNG file in the designated directory.

Examples


example(est.thalf)
x %>% filter(dv > 0) %>% plot_reg(by = 'subject', th = th)

example(est.thalf)
x %>% filter(dv > 0) %>% plot_reg(by = 'subject', th = th)

Perform Non-compartmental Analysis

Description

Consecutively executes the following NCA steps:

correct.loq impute LOQ values
est.thalf calculate lambda_z and half-life
plot_reg plot each regression curve
calc.ctmax calculate Cmax and Tmax
correct.time correct time deviations at critical time points
correct.conc impute missing concentrations at critical time points
tab.corr tabulate data alterations
calc.par calculates parameters not dependent on lambda_z
calc.par.th calculates parameters dependent on lambda_z

Usage

qpNCA(
  x,
  by = character(0),
  nomtimevar = "ntad",
  timevar = "time",
  depvar = "dv",
  bloqvar = "bloq",
  loqvar = "loq",
  loqrule = 1,
  includeCmax = "Y",
  exclvar = NA,
  plotdir = NA,
  timelab = "timevar",
  deplab = "depvar",
  tau = NA,
  tstart = NA,
  tend = NA,
  teval = NA,
  covariates = NA,
  dose = "dose",
  factor = 1,
  reg = "SD",
  ss = "N",
  route = "EV",
  method = 1
)
qpNCA(
  x,
  by = character(0),
  nomtimevar = "ntad",
  timevar = "time",
  depvar = "dv",
  bloqvar = "bloq",
  loqvar = "loq",
  loqrule = 1,
  includeCmax = "Y",
  exclvar = NA,
  plotdir = NA,
  timelab = "timevar",
  deplab = "depvar",
  tau = NA,
  tstart = NA,
  tend = NA,
  teval = NA,
  covariates = NA,
  dose = "dose",
  factor = 1,
  reg = "SD",
  ss = "N",
  route = "EV",
  method = 1
)

Arguments

`x`	input dataset name
`by`	column names in x indicating grouping variables
`nomtimevar`	variable name containing the nominal sampling time after dose
`timevar`	variable name containing the actual sampling time after dose
`depvar`	variable name containing the dependent variable (e.g., concentration)
`bloqvar`	variable name containing the BLOQ flag (0: no, 1: yes)
`loqvar`	variable name containing the LOQ value
`loqrule`	rule number to be applied to the LOQ values in the curve; x$loqrule overrides if provided
`includeCmax`	include Cmax in half-life estimation? (y/n); x$includeCmax overrides if provided
`exclvar`	variable name indicating points to be excluded in half-life estimation (these should have exclvar = 1)
`plotdir`	directory where regression plots (.PNG) will be saved; NA gives default location, NULL skips regression plots
`timelab`	label for time axis in regression plots
`deplab`	label for dependent variable axis in regression plots
`tau`	dosing interval (for multiple dosing); NA (default) if single dose; x$tau overrides
`tstart`	start time of partial AUC (start>0); NA (default) if not requested; x$tstart overrides
`tend`	end time of partial AUC; NA (default) if not requested; x$tend overrides
`teval`	user selected AUC interval (starting at t=0); NA (default) if not requested; x$teval overrides
`covariates`	covariates dataset; Must contain the dose variable
`dose`	variable containing the dose amount
`factor`	conversion factor for CL and V calculation (e.g. dose in mg, conc in ng/mL, factor=1000); x$factor overrides if provided
`reg`	regimen, "SD" or "MD"; x$reg overrides if provided
`ss`	is steady state reached (y/n); x$ss overrides if provided
`route`	route of drug administration ("EV","IVB", "IVI"); x$route overrides if provided
`method`	method for trapezoidal rule; x$method overrides if provided 1: linear up - linear down 2: linear up - logarithmic down 3: linear before first Tmax, logarithmic after first Tmax

Value

(list)

covariates covariates selected with the covariates argument
half_life linear regression parameters
ct_corr the time and concentration corrected dataset
corrections descriptions of the corrections applied
pkpar all estimated PK parameters
plots generated plots

Examples


library(magrittr)
library(dplyr)
library(qpNCA)
x <- Theoph
ntad <- c(0,0.25,0.5,1,2,4,5,7,9,12,24)
for(i in 1:nrow(x)){
  time  <- x$Time[[i]]
  delta <- abs(ntad - time)
  best  <- min(delta)
  index <- match(best, delta)
  nom   <- ntad[[index]]
  x$ntad[[i]] <- nom
}
rm(list = c('time','delta','best','index','nom', 'i','ntad'))
x %<>% rename(time = Time, dv = conc, subject = Subject)
x %<>% mutate(bloq = 0, loq = 0.01, tad = time,excl_th=0,
              subject=as.numeric(subject),ntad=as.numeric(ntad))
x %<>% filter(dv > 0)
covs <- x %>%
  select(subject, Wt, dose = Dose) %>%
  distinct(subject,.keep_all=TRUE) %>%
  mutate(dose = dose * Wt) # see ?Theoph
z <- qpNCA(x, by = 'subject', covariates = covs, exclvar='excl_th')

library(magrittr)
library(dplyr)
library(qpNCA)
x <- Theoph
ntad <- c(0,0.25,0.5,1,2,4,5,7,9,12,24)
for(i in 1:nrow(x)){
  time  <- x$Time[[i]]
  delta <- abs(ntad - time)
  best  <- min(delta)
  index <- match(best, delta)
  nom   <- ntad[[index]]
  x$ntad[[i]] <- nom
}
rm(list = c('time','delta','best','index','nom', 'i','ntad'))
x %<>% rename(time = Time, dv = conc, subject = Subject)
x %<>% mutate(bloq = 0, loq = 0.01, tad = time,excl_th=0,
              subject=as.numeric(subject),ntad=as.numeric(ntad))
x %<>% filter(dv > 0)
covs <- x %>%
  select(subject, Wt, dose = Dose) %>%
  distinct(subject,.keep_all=TRUE) %>%
  mutate(dose = dose * Wt) # see ?Theoph
z <- qpNCA(x, by = 'subject', covariates = covs, exclvar='excl_th')

Tabulate Corrections

Description

Tabulates what records were added, time deviations and concentration imputations were applied, for each subject.

Usage

tab.corr(x, by = character(0), nomtimevar = "time")
tab.corr(x, by = character(0), nomtimevar = "time")

Arguments

`x`	concentration dataset created by the correct.time and correct.conc functions, containing time and conc corrected data
`by`	column names in x indicating grouping variables
`nomtimevar`	column in x containing the nominal time after dose

Value

dataset with applied corrections (rule number and rule text) listed by by-variable(s) and nominal time

Examples


example(correct.conc)
corrtab <- x %>% tab.corr(by = 'subject')
corrtab %>% head

example(correct.conc)
corrtab <- x %>% tab.corr(by = 'subject')
corrtab %>% head

Create Title for Regression Plots

Description

Creates title for regression plots in plot_reg() using by-variables.

Usage

titlefun(x, by)
titlefun(x, by)

Arguments

`x`	dataset containing concentration-time information of the current curve
`by`	column names in x indicating grouping variables

Value

character

Calculate Area Under the Curve Using Trapezoids

Description

Calculates AUC using the trapezoidal method. Assumes data represent a single profile. Despite choice of method, only linear interpolation is used for areas of intervals beginning or ending with y: 0.

Usage

trap(x = NA, y = NA, method = 1)
trap(x = NA, y = NA, method = 1)

Arguments

x

x variable, i.e. time

y

y variable, i.e. concentration

method

method:

1: linear up - linear down
2: linear up - logarithmic down
3: linear before Tmax, logarithmic after Tmax

Value

area (length-one numeric)

Calculate Area Under the Moment Curve Using Trapezoids

Description

Calculates AUMC using the trapezoidal method. Assumes data represent a single profile. Despite choice of method, only linear interpolation is used for areas of intervals beginning or ending with y: 0.

Usage

trapm(x = NA, y = NA, method = 1)
trapm(x = NA, y = NA, method = 1)

Arguments

x

variable names of x coordinates

y

variable names of y coordinates

method

method:

1: linear up - linear down
2: linear up - logarithmic down
3: linear before Tmax, logarithmic after Tmax

Value

area (length-one numeric)

Package 'qpNCA'

Help Index

Calculate Cmax and Tmax

Description

Usage

Arguments

Details

Value

Examples

Calculate NCA Parameters

Description

Usage

Arguments

Value

Examples

Calculate Lambda_z Parameters

Description

Usage

Arguments

Value

Examples

Check qpNCA function arguments for validity

Description

Usage

Arguments

Value

Correct Missing Concentration

Description

Usage

Arguments

Details

Value

Examples

Impute Concentrations Below the Limit of Quantitation

Description

Usage

Arguments

Details

Value

Examples

Correct Concentrations for Time Deviations

Description

Usage

Arguments

Details

Value

Examples

Calculate Lambda_z and Elimination Half-life

Description

Usage

Arguments

Details

Value

Examples

Create File Name for Regression Plots

Description

Usage

Arguments

Value

Interpolate Concentrations

Description

Usage

Arguments

Estimate Lagging and Leading Times and Concentrations

Description

Usage

Arguments

Value

Plot Regression Curves

Description

Usage

Arguments

Details

Value

Examples

Perform Non-compartmental Analysis

Description

Usage

Arguments

Value