Title:	Nonlinear Mixed Effects Models in Population PK/PD, Data
Version:	2.0.9
Description:	Datasets for 'nlmixr2' and 'rxode2'. 'nlmixr2' is used for fitting and comparing nonlinear mixed-effects models in differential equations with flexible dosing information commonly seen in pharmacokinetics and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 <doi:10.1007/s10928-015-9409-1>). Differential equation solving is by compiled C code provided in the 'rxode2' package (Wang, Hallow, and James 2015 <doi:10.1002/psp4.12052>).
License:	GPL (≥ 3)
Encoding:	UTF-8
RoxygenNote:	7.2.3
Depends:	R (≥ 2.10)
LazyData:	true
BugReports:	https://github.com/nlmixr2/nlmixr2data/issues/
URL:	https://nlmixr2.github.io/nlmixr2data/, https://github.com/nlmixr2/nlmixr2data/
NeedsCompilation:	no
Packaged:	2024-01-31 19:55:55 UTC; matt
Author:	Matthew Fidler [aut, cre], Rik Schoemaker [ctb], Kyle Baron [ctb], Thierry Wendling [ctb], Ted Grasella [ctb], C Weil [ctb], Yaning Wang [ctb], R O'Reilly [ctb], David D'Argenio [ctb], Rodriguez-Vera [ctb], D Gaver [ctb], Yuan Xiong [ctb], Wenping Wang [aut]
Maintainer:	Matthew Fidler <matthew.fidler@gmail.com>
Repository:	CRAN
Date/Publication:	2024-01-31 20:40:02 UTC

1 Compartment Model Simulated Data from ACOP 2016

Description

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

Usage

Bolus_1CPT

Format

A data frame with 7,920 rows and 14 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

V

Individual Simulated Volume

CL

Individual Clearance

SS

Steady State

II

Interdose Interval

SD

Single Dose Flag

CMT

Compartment

Details

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%, residual error at 20% and overlapping PK parameters 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.

Source

Schoemaker R, Xiong Y, Wilkins J, Laveille C, Wang W. nlmixr2: an open-source package for pharmacometric modelling in R. ACOP 2016

See Also

Other nlmixr2 datasets: Bolus_1CPTMM, Bolus_2CPTMM, Bolus_2CPT, Infusion_1CPTMM, Infusion_1CPT, Infusion_2CPTMM, Infusion_2CPT, Oral_1CPTMM, Oral_1CPT, Oral_2CPTMM, Oral_2CPT, Wang2007, mavoglurant, nimoData, nmtest, pheno_sd, rats, theo_md, theo_sd, warfarin, wbcSim

1 Compartment Model w/ Michaelis-Menten Elimination

Description

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

Usage

Bolus_1CPTMM

Format

A data frame with 7,920 rows and 14 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

V

Individual Simulated Volume

VM

Individual Vm constant

KM

Individual Km constant

SD

Single Dose Flag

CMT

Compartment

Details

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%, residual error at 20% and overlapping PK parameters 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.

Source

Schoemaker R, Xiong Y, Wilkins J, Laveille C, Wang W. nlmixr2: an open-source package for pharmacometric modelling in R. ACOP 2016

See Also

Other nlmixr2 datasets: Bolus_1CPT, Bolus_2CPTMM, Bolus_2CPT, Infusion_1CPTMM, Infusion_1CPT, Infusion_2CPTMM, Infusion_2CPT, Oral_1CPTMM, Oral_1CPT, Oral_2CPTMM, Oral_2CPT, Wang2007, mavoglurant, nimoData, nmtest, pheno_sd, rats, theo_md, theo_sd, warfarin, wbcSim

2 Compartment Model

Description

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

Usage

Bolus_2CPT

Format

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

Details

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%, residual error at 20% and overlapping PK parameters 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.

Source

Schoemaker R, Xiong Y, Wilkins J, Laveille C, Wang W. nlmixr2: an open-source package for pharmacometric modelling in R. ACOP 2016

See Also

Other nlmixr2 datasets: Bolus_1CPTMM, Bolus_1CPT, Bolus_2CPTMM, Infusion_1CPTMM, Infusion_1CPT, Infusion_2CPTMM, Infusion_2CPT, Oral_1CPTMM, Oral_1CPT, Oral_2CPTMM, Oral_2CPT, Wang2007, mavoglurant, nimoData, nmtest, pheno_sd, rats, theo_md, theo_sd, warfarin, wbcSim

2 Compartment Model with Michaelis-Menten Clearance

Description

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

Usage

Bolus_2CPTMM

Format

A data frame with 7,920 rows and 15 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

V

Individual Central Compartment Volume

VM

Individual Vmax

KM

Individual Km

Q

Individual Q

V2

Individual Peripheral Compartment Volume

SD

Single Dose Flag

CMT

Compartment Indicator

Details

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%, residual error at 20% and overlapping PK parameters 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.

Source

Schoemaker R, Xiong Y, Wilkins J, Laveille C, Wang W. nlmixr2: an open-source package for pharmacometric modelling in R. ACOP 2016

See Also

Other nlmixr2 datasets: Bolus_1CPTMM, Bolus_1CPT, Bolus_2CPT, Infusion_1CPTMM, Infusion_1CPT, Infusion_2CPTMM, Infusion_2CPT, Oral_1CPTMM, Oral_1CPT, Oral_2CPTMM, Oral_2CPT, Wang2007, mavoglurant, nimoData, nmtest, pheno_sd, rats, theo_md, theo_sd, warfarin, wbcSim

1 Compartment Model Simulated Data from ACOP 2016

Description

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

Usage

Infusion_1CPT

Format

A data frame with 7,920 rows and 14 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

V

Individual Simulated Volume

CL

Individual Clearance

SS

Steady State

II

Interdose Interval

SD

Single Dose Flag

RATE

NONMEM Rate

CMT

Compartment

Details

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%, residual error at 20% and overlapping PK parameters 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.

Source

Schoemaker R, Xiong Y, Wilkins J, Laveille C, Wang W. nlmixr2: an open-source package for pharmacometric modelling in R. ACOP 2016

See Also

Other nlmixr2 datasets: Bolus_1CPTMM, Bolus_1CPT, Bolus_2CPTMM, Bolus_2CPT, Infusion_1CPTMM, Infusion_2CPTMM, Infusion_2CPT, Oral_1CPTMM, Oral_1CPT, Oral_2CPTMM, Oral_2CPT, Wang2007, mavoglurant, nimoData, nmtest, pheno_sd, rats, theo_md, theo_sd, warfarin, wbcSim

1 Compartment Model w/MM elimination Simulated Data from ACOP 2016

Description

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

Usage

Infusion_1CPTMM

Format

A data frame with 7,920 rows and 14 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

V

Individual Simulated Volume

KM

Individual Km constant

VM

Individual Vm constant

SD

Single Dose Flag

RATE

NONMEM Rate

CMT

Compartment

Details

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%, residual error at 20% and overlapping PK parameters 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.

Source

Schoemaker R, Xiong Y, Wilkins J, Laveille C, Wang W. nlmixr2: an open-source package for pharmacometric modelling in R. ACOP 2016

See Also

Other nlmixr2 datasets: Bolus_1CPTMM, Bolus_1CPT, Bolus_2CPTMM, Bolus_2CPT, Infusion_1CPT, Infusion_2CPTMM, Infusion_2CPT, Oral_1CPTMM, Oral_1CPT, Oral_2CPTMM, Oral_2CPT, Wang2007, mavoglurant, nimoData, nmtest, pheno_sd, rats, theo_md, theo_sd, warfarin, wbcSim

2 Compartment Model with Infusion Simulated Data from ACOP 2016

Description

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

Usage

Infusion_2CPT

Format

A data frame with 7,920 rows and 17 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

V

Individual Simulated Volume

CL

Individual Clearance

Q

Individual Inter-compartmental Clearance

V2

Individual Peripheral Volume

SS

Steady State

RATE

NONMEM Rate

II

Interdose Interval

SD

Single Dose Flag

CMT

Compartment

Details

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%, residual error at 20% and overlapping PK parameters 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.

Source

Schoemaker R, Xiong Y, Wilkins J, Laveille C, Wang W. nlmixr2: an open-source package for pharmacometric modelling in R. ACOP 2016

See Also

Other nlmixr2 datasets: Bolus_1CPTMM, Bolus_1CPT, Bolus_2CPTMM, Bolus_2CPT, Infusion_1CPTMM, Infusion_1CPT, Infusion_2CPTMM, Oral_1CPTMM, Oral_1CPT, Oral_2CPTMM, Oral_2CPT, Wang2007, mavoglurant, nimoData, nmtest, pheno_sd, rats, theo_md, theo_sd, warfarin, wbcSim

2 Compartment Model w/MM elimination Simulated Data from ACOP 2016

Description

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

Usage

Infusion_2CPTMM

Format

A data frame with 7,920 rows and 14 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

Q

Individual Between Compartment Clearance

V

Individual Simulated Volume

V2

Individual Peripheral Volume

KM

Individual Km constant

VM

Individual Vm constant

SD

Single Dose Flag

RATE

NONMEM Rate

CMT

Compartment

Details

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%, residual error at 20% and overlapping PK parameters 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.

Source

Schoemaker R, Xiong Y, Wilkins J, Laveille C, Wang W. nlmixr2: an open-source package for pharmacometric modelling in R. ACOP 2016

See Also

Other nlmixr2 datasets: Bolus_1CPTMM, Bolus_1CPT, Bolus_2CPTMM, Bolus_2CPT, Infusion_1CPTMM, Infusion_1CPT, Infusion_2CPT, Oral_1CPTMM, Oral_1CPT, Oral_2CPTMM, Oral_2CPT, Wang2007, mavoglurant, nimoData, nmtest, pheno_sd, rats, theo_md, theo_sd, warfarin, wbcSim

1 Compartment Model with Oral Absorption Simulated Data from ACOP 2016

Description

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

Usage

Oral_1CPT

Format

A data frame with 7,920 rows and 15 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

V

Individual Simulated Volume

CL

Individual Clearance

KA

Individual Ka

SS

Steady State

II

Interdose Interval

SD

Single Dose Flag

CMT

Compartment

Details

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%, residual error at 20% and overlapping PK parameters 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.

Source

Schoemaker R, Xiong Y, Wilkins J, Laveille C, Wang W. nlmixr2: an open-source package for pharmacometric modelling in R. ACOP 2016

See Also

Other nlmixr2 datasets: Bolus_1CPTMM, Bolus_1CPT, Bolus_2CPTMM, Bolus_2CPT, Infusion_1CPTMM, Infusion_1CPT, Infusion_2CPTMM, Infusion_2CPT, Oral_1CPTMM, Oral_2CPTMM, Oral_2CPT, Wang2007, mavoglurant, nimoData, nmtest, pheno_sd, rats, theo_md, theo_sd, warfarin, wbcSim

1 Compartment Model w/ Oral Absorption & Michaelis-Menten Elimination

Description

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

Usage

Oral_1CPTMM

Format

A data frame with 7,920 rows and 14 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

KA

Individual Absorption constant

V

Individual Simulated Volume

VM

Individual Vm constant

KM

Individual Km constant

SD

Single Dose Flag

CMT

Compartment

Details

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%, residual error at 20% and overlapping PK parameters 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.

Source

Schoemaker R, Xiong Y, Wilkins J, Laveille C, Wang W. nlmixr2: an open-source package for pharmacometric modelling in R. ACOP 2016

See Also

Other nlmixr2 datasets: Bolus_1CPTMM, Bolus_1CPT, Bolus_2CPTMM, Bolus_2CPT, Infusion_1CPTMM, Infusion_1CPT, Infusion_2CPTMM, Infusion_2CPT, Oral_1CPT, Oral_2CPTMM, Oral_2CPT, Wang2007, mavoglurant, nimoData, nmtest, pheno_sd, rats, theo_md, theo_sd, warfarin, wbcSim

2 Compartment Model with Oral Absorption Simulated Data from ACOP 2016

Description

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

Usage

Oral_2CPT

Format

A data frame with 7,920 rows and 15 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

Q

Individual Inter-compartmental Clearance

V1

Individual Simulated Volume

V2

Individual Simulated Peripheral Volume

CL

Individual Clearance

KA

Individual Ka

SS

Steady State

II

Interdose Interval

SD

Single Dose Flag

CMT

Compartment

Details

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%, residual error at 20% and overlapping PK parameters 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.

Source

Schoemaker R, Xiong Y, Wilkins J, Laveille C, Wang W. nlmixr2: an open-source package for pharmacometric modelling in R. ACOP 2016

See Also

Other nlmixr2 datasets: Bolus_1CPTMM, Bolus_1CPT, Bolus_2CPTMM, Bolus_2CPT, Infusion_1CPTMM, Infusion_1CPT, Infusion_2CPTMM, Infusion_2CPT, Oral_1CPTMM, Oral_1CPT, Oral_2CPTMM, Wang2007, mavoglurant, nimoData, nmtest, pheno_sd, rats, theo_md, theo_sd, warfarin, wbcSim

1 Compartment Model w/ Oral Absorption & Michaelis-Menten Elimination

Description

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

Usage

Oral_2CPTMM

Format

A data frame with 7,920 rows and 14 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

KA

Individual Absorption constant

V1

Individual Simulated Volume

V2

Individual Simulated Perhipheral Volume

Q

Individual Inter-compartmental Clearance

VM

Individual Vm constant

KM

Individual Km constant

SD

Single Dose Flag

CMT

Compartment

Details

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%, residual error at 20% and overlapping PK parameters 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.

Source

Schoemaker R, Xiong Y, Wilkins J, Laveille C, Wang W. nlmixr2: an open-source package for pharmacometric modelling in R. ACOP 2016

See Also

Other nlmixr2 datasets: Bolus_1CPTMM, Bolus_1CPT, Bolus_2CPTMM, Bolus_2CPT, Infusion_1CPTMM, Infusion_1CPT, Infusion_2CPTMM, Infusion_2CPT, Oral_1CPTMM, Oral_1CPT, Oral_2CPT, Wang2007, mavoglurant, nimoData, nmtest, pheno_sd, rats, theo_md, theo_sd, warfarin, wbcSim

Simulated Data Set for comparing objective functions

Description

This is a simulated dataset from Wang2007 where various NONMEM estimation methods (Laplace FO, FOCE with and without interaction) are described.

Usage

Wang2007

Format

A data frame with 20 rows and 3 columns

ID

Simulated Subject ID

Time

Simulated Time

Y

Simulated Value

Source

Table 1 from Wang, Y Derivation of Various NONMEM estimation methods. J Pharmacokinet Pharmacodyn (2007) 34:575-593.

See Also

Other nlmixr2 datasets: Bolus_1CPTMM, Bolus_1CPT, Bolus_2CPTMM, Bolus_2CPT, Infusion_1CPTMM, Infusion_1CPT, Infusion_2CPTMM, Infusion_2CPT, Oral_1CPTMM, Oral_1CPT, Oral_2CPTMM, Oral_2CPT, mavoglurant, nimoData, nmtest, pheno_sd, rats, theo_md, theo_sd, warfarin, wbcSim

Inverse Guassian absorption model

Description

Inverse Guassian absorption model

Usage

invgaussian

Format

A data frame with 32 rows and 6 columns

time

Time of observation

cp

Concentration

Source

Figure 9.7 in D'Argenio DZ, Schumitzky A, and Wang X (2009). "ADAPT 5 User's Guide: Pharmacokinetic/Pharmacodynamic Systems Analysis Software".

Mavoglurant PK data

Description

This was used in a full PBPK model. This one was published for mavoglurant (Wendling et al. 2016).

Usage

mavoglurant

Format

A data frame with 2,678 rows by 14 columns

ID

Subject ID

CMT

Compartment Number

EVID

Event ID

MDV

Missing DV

DV

Dependent Variable, Mavoglurant

AMT

Dose Amount Keyword

TIME

Time (hr)

DOSE

Dose

OCC

Occasion

RATE

Rate

AGE

Age

SEX

Sex

WT

Weight

HT

Height

Source

Wendling et al. 2016

See Also

Other nlmixr2 datasets: Bolus_1CPTMM, Bolus_1CPT, Bolus_2CPTMM, Bolus_2CPT, Infusion_1CPTMM, Infusion_1CPT, Infusion_2CPTMM, Infusion_2CPT, Oral_1CPTMM, Oral_1CPT, Oral_2CPTMM, Oral_2CPT, Wang2007, nimoData, nmtest, pheno_sd, rats, theo_md, theo_sd, warfarin, wbcSim

Parent/Metabolite dataset

Description

Parent/Metabolite dataset

Usage

metabolite

Format

A data frame with 32 rows and 6 columns

time

Time of observation

y1

Parent Concentration

y2

Metabolite Concentration

Source

D'Argenio DZ, Schumitzky A, and Wang X (2009). "ADAPT 5 User's Guide: Pharmacokinetic/Pharmacodynamic Systems Analysis Software".

Nimotuzumab PK data

Description

ID

Subject ID

TIME

Time (hrs)

AMT

Dose Amount Keyword

RATE

Rate

DV

Dependent Variable, Nimotuzumab

TAD

Time After Dose

CMT

Compartment Number

OCC

Occasion

MDV

Missing DV

EVID

Event ID

WGT

Weight

BSA

Body Surface Area

AGE

Age

HGT

Height

DOS

Dose

Usage

nimoData

Format

A data frame with 441 rows by 15 columns

Source

Rodriguez-Vera et al. 2015

See Also

Other nlmixr2 datasets: Bolus_1CPTMM, Bolus_1CPT, Bolus_2CPTMM, Bolus_2CPT, Infusion_1CPTMM, Infusion_1CPT, Infusion_2CPTMM, Infusion_2CPT, Oral_1CPTMM, Oral_1CPT, Oral_2CPTMM, Oral_2CPT, Wang2007, mavoglurant, nmtest, pheno_sd, rats, theo_md, theo_sd, warfarin, wbcSim

One compartment test dataset showing NONMEM 7.4.3 output

Description

This is a example dataset originally created to show how similar mrgsolve and NONMEM were (See ).

Usage

nmtest

Format

A data frame with 7,157 rows and 15 columns

id

NONMEM id

time

NONMEM time

cp

NONMEM cp output from 7.4.3

cmt

cmt specification 1=depot, 2=central

amt

Nonmem dose

evid

NONMEM Event ID

ii

Interdose Interval

ss

Steady state flag

addl

Individual Clearance

rate

Rate of the infusion

lagt

Lag time

bioav

Bioavailability

rat2

Modeled rate when mode == 1

dur2

Duration when mode == 2

mode

Mode = 0 is no modification, modeled rate when mode=1 and modeled duration when mode=2

Details

The original dataset was created by Kyle Baron and is composed of id<100 the id>100 are modifications by Matthew Fidler to benchmark steady state infusions with lag times and other uncommon features.

Note that rxode2/nlmixr2 will not always match these behaviors by default, we choose behaviors that we believe make sense. There are options to make rxode2/nlmixr2 behave more like NONMEM. However behaviors we believe are wrong we do not support.

Author(s)

Kyle Baron & Matthew Fidler

See Also

Other nlmixr2 datasets: Bolus_1CPTMM, Bolus_1CPT, Bolus_2CPTMM, Bolus_2CPT, Infusion_1CPTMM, Infusion_1CPT, Infusion_2CPTMM, Infusion_2CPT, Oral_1CPTMM, Oral_1CPT, Oral_2CPTMM, Oral_2CPT, Wang2007, mavoglurant, nimoData, pheno_sd, rats, theo_md, theo_sd, warfarin, wbcSim

Single Dose Phenobarbitol PK/PD

Description

This is from a PK study in neonatal infants. They received multiple doses of phenobarbital for seizure prevention.

Usage

pheno_sd

Format

A data frame with 744 rows and 8 columns

ID

Infant ID

TIME

Time (hr)

AMT

Dose (ug/kg)

WT

Weight (kg)

APGR

A 5-minute Apgar score to measure infant health

DV

The concentration of phenobarbitol in the serum (ug/mL)

MDV

If the dependent variable (DV) is missing; 0 for observations, 1 for doses

EVID

Event ID

Details

The data were originally given in Grasela and Donn(1985) and are analyzed in Boeckmann, Sheiner and Beal (1994), in Davidian and Giltinan (1995), and in Littell et al. (1996).

Source

Pinheiro, J. C. and Bates, D. M. (2000), Mixed-Effects Models in S and S-PLUS, Springer, New York. (Appendix A.23)

Davidian, M. and Giltinan, D. M. (1995), Nonlinear Models for Repeated Measurement Data, Chapman and Hall, London. (section 6.6)

Grasela and Donn (1985), Neonatal population pharmacokinetics of phenobarbital derived from routine clinical data, Developmental Pharmacology and Therapeutics, 8, 374-383.

Boeckmann, A. J., Sheiner, L. B., and Beal, S. L. (1994), NONMEM Users Guide: Part V, University of California, San Francisco.

Littell, R. C., Milliken, G. A., Stroup, W. W. and Wolfinger, R. D. (1996), SAS System for Mixed Models, SAS Institute, Cary, NC.

See Also

Other nlmixr2 datasets: Bolus_1CPTMM, Bolus_1CPT, Bolus_2CPTMM, Bolus_2CPT, Infusion_1CPTMM, Infusion_1CPT, Infusion_2CPTMM, Infusion_2CPT, Oral_1CPTMM, Oral_1CPT, Oral_2CPTMM, Oral_2CPT, Wang2007, mavoglurant, nimoData, nmtest, rats, theo_md, theo_sd, warfarin, wbcSim

Pump failure example dataset

Description

The records the number of failures and operation time for groups of 10 pumps.

Usage

pump

Format

A data frame with 10 rows and 5 columns

y

Number of pump failures

t

Failure Time

group

Continuous Operation (=1) or Intermittent Operation(=2)

ID

ID for group of 10 pumps

logtstd

Centered operation times

Source

https://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_nlmixed_sect040.htm

References

Gaver, D. P. and O'Muircheartaigh, I. G. (1987), "Robust Empirical Bayes Analysis of Event Rates," Technometrics, 29, 1-15.

Pregnant Rat Diet Experiment

Description

16 pregnant rats have a control diet, and 16 have a chemically treated diet. The litter size for each rat is recorded after 4 and 21 days. This dataset is used in the SAS Probit-model with binomial data, and saved in the nlmixr2 package as rats.

Usage

rats

Format

A data frame with 32 rows and 6 columns

trt

Treatment; c= control diet; t=treated diet

m

Litter size after 4 days

x

Litter size after 21 days

x1

Indicator for trt=c

x2

Indicator for trt=t

ID

Rat ID

Source

https://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_nlmixed_sect040.htm

References

Weil, C.S., 1970. Selection of the valid number of sampling units and a consideration of their combination in toxicological studies involving reproduction, teratogenesis or carcinogenesis. Fd. Cosmet. Toxicol. 8, 177-182.

Williams, D.A., 1975. The analysis of binary responses from toxicological experiments involving reproduction and teratogenicity. Biometrics 31, 949-952.

McCulloch, C. E. (1994), "Maximum Likelihood Variance Components Estimation for Binary Data," Journal of the American Statistical Association, 89, 330 - 335.

Ochi, Y. and Prentice, R. L. (1984), "Likelihood Inference in a Correlated Probit Regression Model," Biometrika, 71, 531-543.

See Also

Other nlmixr2 datasets: Bolus_1CPTMM, Bolus_1CPT, Bolus_2CPTMM, Bolus_2CPT, Infusion_1CPTMM, Infusion_1CPT, Infusion_2CPTMM, Infusion_2CPT, Oral_1CPTMM, Oral_1CPT, Oral_2CPTMM, Oral_2CPT, Wang2007, mavoglurant, nimoData, nmtest, pheno_sd, theo_md, theo_sd, warfarin, wbcSim

Multiple dose theophylline PK data

Description

This data set starts with the day 1 concentrations of the theophylline data that is included in the nlme/NONMEM. After day 7 concentrations were simulated with once a day regimen for 7 days (QD).

Usage

theo_md

Format

A data frame with 348 rows by 7 columns

ID

Subject ID

TIME

Time (hr)

DV

Dependent Variable, theophylline concentration (mg/L)

AMT

Dose Amount (kg)

EVID

rxode2/nlmixr2 event ID (not NONMEM event IDs)

CMT

Compartment number

WT

Body weight (kg)

Source

NONMEM/nlme

See Also

Other nlmixr2 datasets: Bolus_1CPTMM, Bolus_1CPT, Bolus_2CPTMM, Bolus_2CPT, Infusion_1CPTMM, Infusion_1CPT, Infusion_2CPTMM, Infusion_2CPT, Oral_1CPTMM, Oral_1CPT, Oral_2CPTMM, Oral_2CPT, Wang2007, mavoglurant, nimoData, nmtest, pheno_sd, rats, theo_sd, warfarin, wbcSim

Multiple dose theophylline PK data

Description

This data set is the day 1 concentrations of the theophylline data that is included in the nlme/NONMEM.

Usage

theo_sd

Format

A data frame with 144 rows by 7 columns

ID

Subject ID

TIME

Time (hr)

DV

Dependent Variable, theophylline concentration (mg/L)

AMT

Dose Amount (mg)

EVID

rxode2/nlmixr2 event ID (not NONMEM event IDs)

CMT

Compartment Number

WT

Body weight (kg)

Source

NONMEM/nlme

See Also

Other nlmixr2 datasets: Bolus_1CPTMM, Bolus_1CPT, Bolus_2CPTMM, Bolus_2CPT, Infusion_1CPTMM, Infusion_1CPT, Infusion_2CPTMM, Infusion_2CPT, Oral_1CPTMM, Oral_1CPT, Oral_2CPTMM, Oral_2CPT, Wang2007, mavoglurant, nimoData, nmtest, pheno_sd, rats, theo_md, warfarin, wbcSim

Warfarin PK/PD data

Description

Warfarin PK/PD data

Usage

warfarin

Format

A data frame with 519 rows and 9 columns

id

Patient identifier (n=32)

time

Time (h)

amt

Total drug administered (mg)

dv

Warfarin concentrations (mg/L) or PCA measurement

dvid

Dependent identifier Information (cp: Dose or PK, pca: PCA, factor)

evid

Event identifier

wt

Weight (kg)

age

Age (yr)

sex

Sex (male or female, factor)

Source

Funaki T, Holford N, Fujita S (2018). Population PKPD analysis using nlmixr2 and NONMEM. PAGJA 2018

References

O'Reilly RA, Aggeler PM, Leong LS. Studies of the coumarin anticoagulant drugs: The pharmacodynamics of warfarin in man. Journal of Clinical Investigation 1963; 42(10): 1542-1551

O'Reilly RA, Aggeler PM. Studies on coumarin anticoagulant drugs Initiation of warfarin therapy without a loading dose. Circulation 1968; 38: 169-177.

See Also

Other nlmixr2 datasets: Bolus_1CPTMM, Bolus_1CPT, Bolus_2CPTMM, Bolus_2CPT, Infusion_1CPTMM, Infusion_1CPT, Infusion_2CPTMM, Infusion_2CPT, Oral_1CPTMM, Oral_1CPT, Oral_2CPTMM, Oral_2CPT, Wang2007, mavoglurant, nimoData, nmtest, pheno_sd, rats, theo_md, theo_sd, wbcSim

Simulated Friberg Myelosuppression model (Yuan Xiong)

Description

ID

Subject ID

TIME

Time (hrs)

RATE

Rate

AMT

Dose Amount Keyword

DV

Dependent Variable, WBC

CMT

Compartment Number

V2I

Input Peripheral Volume

V1I

Input Central Volume

V1I

Input Clearance

EVID

nlmixr2/rxode2 classic evid

Usage

wbcSim

Format

An object of class data.frame with 280 rows and 10 columns.

Source

Simulated Data for WBC pac ddmore model

See Also

Other nlmixr2 datasets: Bolus_1CPTMM, Bolus_1CPT, Bolus_2CPTMM, Bolus_2CPT, Infusion_1CPTMM, Infusion_1CPT, Infusion_2CPTMM, Infusion_2CPT, Oral_1CPTMM, Oral_1CPT, Oral_2CPTMM, Oral_2CPT, Wang2007, mavoglurant, nimoData, nmtest, pheno_sd, rats, theo_md, theo_sd, warfarin