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")

Arguments

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

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
)

Arguments

Value

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

Examples

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"
)

Arguments

Value

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

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)

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
)

Arguments

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
)

Arguments

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:

Value

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

Examples

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
)

Arguments

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

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
)

Arguments

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:

Value

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

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

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
)

Arguments

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

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)

Arguments

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)

Arguments

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,
  ...
)

Arguments

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,
  ...
)

Arguments

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)

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
)

Arguments

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')

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")

Arguments

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

Create Title for Regression Plots

Description

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

Usage

titlefun(x, by)

Arguments

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)

Arguments

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)

Arguments

Value

area (length-one numeric)