Nothing
library(dplyr) library(qpNCA) library(knitr)
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] }
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) 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))
# Create a covariates file, containing at least the dose given cov = input.data %>% distinct(subject,dose) nca = qpNCA( input.data, by = "subject", nomtimevar = "ntad", timevar = "tad", depvar = "conc", bloqvar = "bloq", loqvar = "loq", loqrule = 1, includeCmax = "Y", exclvar = "excl_th", plotdir = NA, timelab = "Time (h)", deplab = "Conc (ng/mL)", tau = 24, tstart = 4, tend = 9, teval = 12, covariates = cov, dose = "dose", factor = 1, reg = "sd", ss = "n", route = "EV", method = 1 )
# Covariates: nca$covariates %>% kable() # Corrections applied: nca$corrections %>% kable() # half-life estimation: nca$half_life %>% kable() # PK parameters: nca$pkpar %>% kable() # Regression plots: nca$plots
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