Perform an NCA based on a NONMEM-style dataset
Arguments
- data
data.frame with time and dv columns
- dose
dose amount
- tau
dosing frequency, default is 24.
- method
linear,log_linear(default), orlog_log- scale
list with scaling for auc and concentration (
conc)- dv_min
minimum concentrations, lower observations will be set to this value
- t_inf
infusion time, defaults to 0
- fit_samples
vector of sample indexes used in fit to calculate elimination rate, e.g.
c(3,4,5). If not specified (default), it will evaluate which of the last n samples shows the largest adjusted R^2 when log-transformed data is fitted using linear regression, and use those samples in the estimation of the elimination rate.- weights
vector of weights to be used in linear regression (same size as specified concentration data), or function with concentration as argument.
- extend
perform an 'extended' NCA, i.e. for the calculation of the AUCs, back-extend to the expected true Cmax to also include that area.
- has_baseline
does the included data include a baseline? If
FALSE, baseline is set to zero.- route
administration route,
iv(intravenous, default),oral,sc(sub-cutaneous), orim(intra-muscular).
Value
Returns a list of three lists:
pkLists pk parameters.
kel: elimination constantt_12: half-lifev: distribution volumecl: clearance
descriptiveLists exposure parameters.
cav_t: the average concentration between the first observation and the last observation without extrapolating to taucav_tau: the average concentration from 0 to taucmin: the extrapolated concentration attime = tauc_max_true: only available ifextend = TRUE, the extrapolated peak concentrationc_max: only available ifextend = FALSE, the observed maximum concentrationauc_inf: the extrapolated AUC as time goes to infinityauc_24: the extrapolated AUC after 24 hours, provided no further doses are administeredauc_tau: the extrapolated AUC at the end of the dosing intervalauc_t: the AUC at the time of the last observation
settingsLists dosing information.
dose: dose quantitytau: dosing interval
Examples
data <- data.frame(time = c(0, 2, 4, 6, 8, 12, 16),
dv = c(0, 10, 14, 11, 9, 5, 1.5))
nca(data, t_inf = 2)
#> value
#> kel 0.1807
#> t_12 3.8358
#> v 4.1763
#> cl 0.7547
#> tmax 4.0000
#> cav_t 8.2119
#> cav_tau 5.7390
#> c_min 0.3534
#> c_max_true 20.0949
#> auc_inf 139.6910
#> auc_24 137.7354
#> auc_tau 137.7354
#> auc_t 131.3902
#> auc_pre 14.0000