calc.par.th: Calculate Lambda_z Parameters
In qpNCA: Noncompartmental Pharmacokinetic Analysis by qPharmetra

Description Usage Arguments Value Examples

Calculates PK parameters that need lambda_z.

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

`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

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.

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)