A simulated pharmacokinetic dataset for the fictional drug examplomycin,
intended as a worked example for aggregate data modelling with admixr2.
The dataset contains 500 subjects, each with 9 observation time points,
generated from a two-compartment model with first-order absorption.
Format
A data frame with 5000 rows and 6 columns:
ID: Subject identifier (integer, 1–500).TIME: Time after dose (hours).DV: Observed plasma concentration (mg/L).AMT: Dose amount (mg); 100 for dosing records, 0 otherwise.EVID: Event type (101 = dose, 0 = observation).CMT: Compartment (1 = depot, 2 = central).
Source
Generated from a two-compartment PK model using rxode2::rxSolve().
See vignette("admixr2") for a full modelling example.
Details
True population parameters:
| Parameter | Value |
| CL (L/hr) | 5 |
| V1 (L) | 10 |
| V2 (L) | 30 |
| Q (L/hr) | 10 |
| ka (1/hr) | 1 |
| IIV (all, SD on log scale) | 0.3 |
| Proportional error (SD) | 0.2 |
Single oral dose of 100 mg; sampling at 0.1, 0.25, 0.5, 1, 2, 3, 5, 8, and 12 hours post-dose.
Examples
data("examplomycin")
head(examplomycin)
#> ID TIME DV AMT EVID CMT
#> 1 460 0.00 0.000 100 101 1
#> 2 460 0.10 0.752 0 0 2
#> 3 460 0.25 1.932 0 0 2
#> 4 460 0.50 3.694 0 0 2
#> 5 460 1.00 3.479 0 0 2
#> 6 460 2.00 4.003 0 0 2
# Compute aggregate statistics
obs <- examplomycin[examplomycin$EVID == 0, ]
obs <- obs[order(obs$ID, obs$TIME), ]
times <- sort(unique(obs$TIME))
E <- sapply(times, function(t) mean(obs$DV[obs$TIME == t]))
round(E, 3)
#> [1] 0.966 1.939 2.788 3.025 2.258 1.651 1.063 0.751 0.512