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Load libraries

library(dplyr)
library(qpNCA)
library(knitr)

Define mutate_cond and locf functions

mutate_cond <- function (.data, condition, ..., envir = parent.frame()){
  condition <- eval(substitute(condition), .data, envir)
  if(!any(condition))return(.data) # do nothing if nothing to do
  .data[condition, ] <- .data[condition, ] %>% mutate(...)
  .data
}
locf <- function(x){
  good <- !is.na(x)
  positions <- seq(length(x))
  good.positions <- good * positions
  last.good.position <- cummax(good.positions)
  last.good.position[last.good.position == 0] <- NA
  x[last.good.position]
}

Check out Theoph data and prepare data for NCA

We use the internal Theoph dataset as input file, modify the data and add necessary columns
Furthermore, we introduce some missing values, LOQ values and time deviations

head(Theoph) %>% kable()

input.data <- Theoph

#we need nominal time variable for some tasks.
ntad <- data.frame(rn=c(1:11),ntad=c(0,0.25,0.5,1,2,4,5,7,9,12,24))

input.data %<>% 
           group_by(Subject) %>%
           mutate(subject=as.numeric(Subject),
                  rn=row_number(),
                  dose=Dose*Wt,
                  bloq=ifelse(conc==0,1,0),
                  loq=0.1,
                  excl_th=0
                  ) %>%
           left_join(ntad) %>%
           ungroup %>%
           arrange(subject,ntad) %>%
           select(subject,ntad,tad=Time,conc,dose,bloq,loq,excl_th)

## Joining, by = "rn"

input.data %<>%
           mutate_cond(condition=subject==2&ntad%in%c(24),conc=NA) %>%
           mutate_cond(condition=subject==4&ntad%in%c(9),conc=NA) %>%
           mutate_cond(condition=subject==3&ntad==9,excl_th=1) %>%
           mutate_cond(condition=subject==6&ntad==24,conc=0,bloq=1) %>%
           filter(!(subject==5&ntad==12))

Impute LOQ values

loqed.data = input.data %>%
  correct.loq(
  by = "subject",
  nomtimevar = "ntad",
  timevar = "tad",
  depvar = "conc",
  bloqvar = "bloq",
  loqvar = "loq",
  loqrule = 1
)

# Result:

loqed.data %>% filter(loqrule.nr!="") %>% select(subject,ntad,conc,loqrule.nr,loqrule.txt) %>% kable()

Estimation of terminal half-life

th = loqed.data %>% 
     est.thalf(
     by = "subject",
     timevar = "tad",
     depvar = "conc",
     includeCmax = "Y",
     exclvar = "excl_th"
     )

# Result:

head(th) %>% kable()

Plot individual regression plots

plot_reg(
  loqed.data,
  by = "subject",
  th = th,
  bloqvar = "bloq",
  timevar = "tad",
  depvar = "conc",
  timelab = "Time (h)",
  deplab = "Conc (ng/mL)",
  exclvar = "excl_th",
  plotdir = NA
)

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Calculate Cmax and Tmax

ctmax = input.data %>% calc.ctmax(
  by = "subject",
  timevar="tad",
  depvar="conc"
)

# Result:

head(ctmax) %>% kable()

Correct time deviations at critical time points

ct.data= loqed.data %>%
    correct.time(
    by="subject",
    nomtimevar="ntad",
    timevar="tad",
    depvar="conc",
    th=th,
    tau=24,
    tstart=4,
    tend=9,
    teval=12,
    reg="sd",
    method=1
  ) 

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# Result:

ct.data %>% filter(subject<=5&(trule.nr!=""|create.nr!="")) %>% select(subject,ntad,conc,applies.to.time,trule.nr,trule.txt,create.txt) %>% kable()

Impute missing concentrations at critical time points

cc.ct.data = ct.data %>%
    correct.conc(
    by ="subject",
    nomtimevar="ntad",
    tau=24,
    tstart=4,
    tend=9,
    teval=12,
    th=th,
    reg="sd",
    ss="n",
    route="EV",
    method=1
  )

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# Result:

cc.ct.data %>% filter(crule.nr!="") %>% select(subject,ntad,conc,applies.to.conc,crule.nr,crule.txt) %>% kable()

Tabulate corrections

tab_corr = cc.ct.data %>% 
     tab.corr(
       by="subject",
       nomtimevar="ntad"
     )

# Result:

tab_corr %>% kable()

Calculate PK parameters NOT based on lambda_z

par = cc.ct.data %>%
      calc.par(by = 'subject',
               tau=24,
               teval=12,
               tstart=4,
               tend=9,
               route="EV",
               method=1)

# Result:

head(par) %>% kable()

Calculate PK parameters based on lambda_z

# Create a covariates file, containing at least the dose given

cov = input.data %>%
      distinct(subject,dose)

par <- par %>% 
  calc.par.th(
    by="subject",
    th=th ,
    covariates=cov,
    dose="dose",
    factor=1,  
    reg="sd",
    ss="n"
    )

# Result:

head(par) %>% kable()

Combine All NCA parameters in one data frame

par_all = left_join(par, ctmax)

## Joining, by = "subject"

# Result:

head(par_all) %>% kable()

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