Bolus_2CPT | R Documentation |

This is a simulated dataset from the ACOP 2016 poster. All Datasets were simulated with the following methods.

Bolus_2CPT

A data frame with 7,920 rows and 16 columns

- ID
Simulated Subject ID

- TIME
Simulated Time

- DV
Simulated Dependent Variable

- LNDV
Simulated log(Dependent Variable)

- MDV
Missing DV data item

- AMT
Dosing AMT

- EVID
NONMEM Event ID

- DOSE
Dose

- V1
Individual Central Compartment Volume

- CL
Individual Clearance

- Q
Individual Between Compartment Clearance

- V2
Periperial Volume

- SS
Steady State Flag

- II
Interdose interval

- SD
Single Dose Flag

- CMT
Compartment Indicator

Richly sampled profiles were simulated for 4 different dose levels (10, 30, 60 and 120 mg) of 30 subjects each as single dose (over 72h), multiple dose (4 daily doses), single and multiple dose combined, and steady state dosing, for a range of test models: 1- and 2-compartment disposition, with and without 1st order absorption, with either linear or Michaelis-Menten (MM) clearance(MM without steady state dosing). This provided a total of 42 test cases. All inter-individual variabilities (IIVs) were set at 30 were the same for all models. A similar set of models was previously used to compare NONMEM and Monolix4. Estimates of population parameters, standard errors for fixed-effect parameters, and run times were compared both for closed-form solutions and using ODEs. Additionally, a sparse data estimation situation was investigated where 500 datasets of 600 subjects each (150 per dose) were generated consisting of 4 random time point samples in 24 hours per subject, using a first-order absorption, 1-compartment disposition, linear elimination model.

Schoemaker R, Xiong Y, Wilkins J, Laveille C, Wang W. nlmixr: an open-source package for pharmacometric modelling in R. ACOP 2016

Other nlmixr datasets:
`Bolus_1CPTMM`

,
`Bolus_1CPT`

,
`Bolus_2CPTMM`

,
`Infusion_1CPT`

,
`Oral_1CPT`

,
`Wang2007`

,
`pheno_sd`

,
`rats`

,
`theo_md`

,
`theo_sd`

,
`warfarin`

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