| Title: | Nonlinear Mixed Effects Models in Population PK/PD, Data |
|---|---|
| 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>). |
| Authors: | 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 <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 2.0.9 |
| Built: | 2024-08-30 04:51:18 UTC |
| Source: | https://github.com/nlmixr2/nlmixr2data |
This is a simulated dataset from the ACOP 2016 poster. All Datasets were simulated with the following methods.
Bolus_1CPTBolus_1CPT
A data frame with 7,920 rows and 14 columns
Simulated Subject ID
Simulated Time
Simulated Dependent Variable
Simulated log(Dependent Variable)
Missing DV data item
Dosing AMT
NONMEM Event ID
Dose
Individual Simulated Volume
Individual Clearance
Steady State
Interdose Interval
Single Dose Flag
Compartment
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.
Schoemaker R, Xiong Y, Wilkins J, Laveille C, Wang W. nlmixr2: an open-source package for pharmacometric modelling in R. ACOP 2016
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
This is a simulated dataset from the ACOP 2016 poster. All Datasets were simulated with the following methods.
Bolus_1CPTMMBolus_1CPTMM
A data frame with 7,920 rows and 14 columns
Simulated Subject ID
Simulated Time
Simulated Dependent Variable
Simulated log(Dependent Variable)
Missing DV data item
Dosing AMT
NONMEM Event ID
Dose
Individual Simulated Volume
Individual Vm constant
Individual Km constant
Single Dose Flag
Compartment
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.
Schoemaker R, Xiong Y, Wilkins J, Laveille C, Wang W. nlmixr2: an open-source package for pharmacometric modelling in R. ACOP 2016
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
This is a simulated dataset from the ACOP 2016 poster. All Datasets were simulated with the following methods.
Bolus_2CPTBolus_2CPT
A data frame with 7,920 rows and 16 columns
Simulated Subject ID
Simulated Time
Simulated Dependent Variable
Simulated log(Dependent Variable)
Missing DV data item
Dosing AMT
NONMEM Event ID
Dose
Individual Central Compartment Volume
Individual Clearance
Individual Between Compartment Clearance
Periperial Volume
Steady State Flag
Interdose interval
Single Dose Flag
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%, 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.
Schoemaker R, Xiong Y, Wilkins J, Laveille C, Wang W. nlmixr2: an open-source package for pharmacometric modelling in R. ACOP 2016
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
This is a simulated dataset from the ACOP 2016 poster. All Datasets were simulated with the following methods.
Bolus_2CPTMMBolus_2CPTMM
A data frame with 7,920 rows and 15 columns
Simulated Subject ID
Simulated Time
Simulated Dependent Variable
Simulated log(Dependent Variable)
Missing DV data item
Dosing AMT
NONMEM Event ID
Dose
Individual Central Compartment Volume
Individual Vmax
Individual Km
Individual Q
Individual Peripheral Compartment Volume
Single Dose Flag
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%, 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.
Schoemaker R, Xiong Y, Wilkins J, Laveille C, Wang W. nlmixr2: an open-source package for pharmacometric modelling in R. ACOP 2016
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
This is a simulated dataset from the ACOP 2016 poster. All Datasets were simulated with the following methods.
Infusion_1CPTInfusion_1CPT
A data frame with 7,920 rows and 14 columns
Simulated Subject ID
Simulated Time
Simulated Dependent Variable
Simulated log(Dependent Variable)
Missing DV data item
Dosing AMT
NONMEM Event ID
Dose
Individual Simulated Volume
Individual Clearance
Steady State
Interdose Interval
Single Dose Flag
NONMEM Rate
Compartment
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.
Schoemaker R, Xiong Y, Wilkins J, Laveille C, Wang W. nlmixr2: an open-source package for pharmacometric modelling in R. ACOP 2016
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
This is a simulated dataset from the ACOP 2016 poster. All Datasets were simulated with the following methods.
Infusion_1CPTMMInfusion_1CPTMM
A data frame with 7,920 rows and 14 columns
Simulated Subject ID
Simulated Time
Simulated Dependent Variable
Simulated log(Dependent Variable)
Missing DV data item
Dosing AMT
NONMEM Event ID
Dose
Individual Simulated Volume
Individual Km constant
Individual Vm constant
Single Dose Flag
NONMEM Rate
Compartment
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.
Schoemaker R, Xiong Y, Wilkins J, Laveille C, Wang W. nlmixr2: an open-source package for pharmacometric modelling in R. ACOP 2016
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
This is a simulated dataset from the ACOP 2016 poster. All Datasets were simulated with the following methods.
Infusion_2CPTInfusion_2CPT
A data frame with 7,920 rows and 17 columns
Simulated Subject ID
Simulated Time
Simulated Dependent Variable
Simulated log(Dependent Variable)
Missing DV data item
Dosing AMT
NONMEM Event ID
Dose
Individual Simulated Volume
Individual Clearance
Individual Inter-compartmental Clearance
Individual Peripheral Volume
Steady State
NONMEM Rate
Interdose Interval
Single Dose Flag
Compartment
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.
Schoemaker R, Xiong Y, Wilkins J, Laveille C, Wang W. nlmixr2: an open-source package for pharmacometric modelling in R. ACOP 2016
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
This is a simulated dataset from the ACOP 2016 poster. All Datasets were simulated with the following methods.
Infusion_2CPTMMInfusion_2CPTMM
A data frame with 7,920 rows and 14 columns
Simulated Subject ID
Simulated Time
Simulated Dependent Variable
Simulated log(Dependent Variable)
Missing DV data item
Dosing AMT
NONMEM Event ID
Dose
Individual Between Compartment Clearance
Individual Simulated Volume
Individual Peripheral Volume
Individual Km constant
Individual Vm constant
Single Dose Flag
NONMEM Rate
Compartment
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.
Schoemaker R, Xiong Y, Wilkins J, Laveille C, Wang W. nlmixr2: an open-source package for pharmacometric modelling in R. ACOP 2016
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
Inverse Guassian absorption model
invgaussianinvgaussian
A data frame with 32 rows and 6 columns
Time of observation
Concentration
Figure 9.7 in D'Argenio DZ, Schumitzky A, and Wang X (2009). "ADAPT 5 User's Guide: Pharmacokinetic/Pharmacodynamic Systems Analysis Software".
This was used in a full PBPK model. This one was published for mavoglurant (Wendling et al. 2016).
mavoglurantmavoglurant
A data frame with 2,678 rows by 14 columns
Subject ID
Compartment Number
Event ID
Missing DV
Dependent Variable, Mavoglurant
Dose Amount Keyword
Time (hr)
Dose
Occasion
Rate
Age
Sex
Weight
Height
Wendling et al. 2016
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
metabolitemetabolite
A data frame with 32 rows and 6 columns
Time of observation
Parent Concentration
Metabolite Concentration
D'Argenio DZ, Schumitzky A, and Wang X (2009). "ADAPT 5 User's Guide: Pharmacokinetic/Pharmacodynamic Systems Analysis Software".
Subject ID
Time (hrs)
Dose Amount Keyword
Rate
Dependent Variable, Nimotuzumab
Time After Dose
Compartment Number
Occasion
Missing DV
Event ID
Weight
Body Surface Area
Age
Height
Dose
nimoDatanimoData
A data frame with 441 rows by 15 columns
Rodriguez-Vera et al. 2015
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
This is a example dataset originally created to show how similar
mrgsolve and NONMEM were (See ).
nmtestnmtest
A data frame with 7,157 rows and 15 columns
NONMEM id
NONMEM time
NONMEM cp output from 7.4.3
cmt specification 1=depot, 2=central
Nonmem dose
NONMEM Event ID
Interdose Interval
Steady state flag
Individual Clearance
Rate of the infusion
Lag time
Bioavailability
Modeled rate when mode == 1
Duration when mode == 2
Mode = 0 is no modification, modeled rate when mode=1 and modeled duration when mode=2
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.
Kyle Baron & Matthew Fidler
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
This is a simulated dataset from the ACOP 2016 poster. All Datasets were simulated with the following methods.
Oral_1CPTOral_1CPT
A data frame with 7,920 rows and 15 columns
Simulated Subject ID
Simulated Time
Simulated Dependent Variable
Simulated log(Dependent Variable)
Missing DV data item
Dosing AMT
NONMEM Event ID
Dose
Individual Simulated Volume
Individual Clearance
Individual Ka
Steady State
Interdose Interval
Single Dose Flag
Compartment
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.
Schoemaker R, Xiong Y, Wilkins J, Laveille C, Wang W. nlmixr2: an open-source package for pharmacometric modelling in R. ACOP 2016
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
This is a simulated dataset from the ACOP 2016 poster. All Datasets were simulated with the following methods.
Oral_1CPTMMOral_1CPTMM
A data frame with 7,920 rows and 14 columns
Simulated Subject ID
Simulated Time
Simulated Dependent Variable
Simulated log(Dependent Variable)
Missing DV data item
Dosing AMT
NONMEM Event ID
Dose
Individual Absorption constant
Individual Simulated Volume
Individual Vm constant
Individual Km constant
Single Dose Flag
Compartment
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.
Schoemaker R, Xiong Y, Wilkins J, Laveille C, Wang W. nlmixr2: an open-source package for pharmacometric modelling in R. ACOP 2016
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
This is a simulated dataset from the ACOP 2016 poster. All Datasets were simulated with the following methods.
Oral_2CPTOral_2CPT
A data frame with 7,920 rows and 15 columns
Simulated Subject ID
Simulated Time
Simulated Dependent Variable
Simulated log(Dependent Variable)
Missing DV data item
Dosing AMT
NONMEM Event ID
Dose
Individual Inter-compartmental Clearance
Individual Simulated Volume
Individual Simulated Peripheral Volume
Individual Clearance
Individual Ka
Steady State
Interdose Interval
Single Dose Flag
Compartment
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.
Schoemaker R, Xiong Y, Wilkins J, Laveille C, Wang W. nlmixr2: an open-source package for pharmacometric modelling in R. ACOP 2016
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
This is a simulated dataset from the ACOP 2016 poster. All Datasets were simulated with the following methods.
Oral_2CPTMMOral_2CPTMM
A data frame with 7,920 rows and 14 columns
Simulated Subject ID
Simulated Time
Simulated Dependent Variable
Simulated log(Dependent Variable)
Missing DV data item
Dosing AMT
NONMEM Event ID
Dose
Individual Absorption constant
Individual Simulated Volume
Individual Simulated Perhipheral Volume
Individual Inter-compartmental Clearance
Individual Vm constant
Individual Km constant
Single Dose Flag
Compartment
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.
Schoemaker R, Xiong Y, Wilkins J, Laveille C, Wang W. nlmixr2: an open-source package for pharmacometric modelling in R. ACOP 2016
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
This is from a PK study in neonatal infants. They received multiple doses of phenobarbital for seizure prevention.
pheno_sdpheno_sd
A data frame with 744 rows and 8 columns
Infant ID
Time (hr)
Dose (ug/kg)
Weight (kg)
A 5-minute Apgar score to measure infant health
The concentration of phenobarbitol in the serum (ug/mL)
If the dependent variable (DV) is missing; 0 for observations, 1 for doses
Event ID
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).
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.
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
The records the number of failures and operation time for groups of 10 pumps.
pumppump
A data frame with 10 rows and 5 columns
Number of pump failures
Failure Time
Continuous Operation (=1) or Intermittent Operation(=2)
ID for group of 10 pumps
Centered operation times
Gaver, D. P. and O'Muircheartaigh, I. G. (1987), "Robust Empirical Bayes Analysis of Event Rates," Technometrics, 29, 1-15.
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.
ratsrats
A data frame with 32 rows and 6 columns
Treatment; c= control diet; t=treated diet
Litter size after 4 days
Litter size after 21 days
Indicator for trt=c
Indicator for trt=t
Rat ID
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.
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
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).
theo_mdtheo_md
A data frame with 348 rows by 7 columns
Subject ID
Time (hr)
Dependent Variable, theophylline concentration (mg/L)
Dose Amount (kg)
rxode2/nlmixr2 event ID (not NONMEM event IDs)
Compartment number
Body weight (kg)
NONMEM/nlme
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
This data set is the day 1 concentrations of the theophylline data that is included in the nlme/NONMEM.
theo_sdtheo_sd
A data frame with 144 rows by 7 columns
Subject ID
Time (hr)
Dependent Variable, theophylline concentration (mg/L)
Dose Amount (mg)
rxode2/nlmixr2 event ID (not NONMEM event IDs)
Compartment Number
Body weight (kg)
NONMEM/nlme
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
This is a simulated dataset from Wang2007 where various NONMEM estimation methods (Laplace FO, FOCE with and without interaction) are described.
Wang2007Wang2007
A data frame with 20 rows and 3 columns
Simulated Subject ID
Simulated Time
Simulated Value
Table 1 from Wang, Y Derivation of Various NONMEM estimation methods. J Pharmacokinet Pharmacodyn (2007) 34:575-593.
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
Warfarin PK/PD data
warfarinwarfarin
A data frame with 519 rows and 9 columns
Patient identifier (n=32)
Time (h)
Total drug administered (mg)
Warfarin concentrations (mg/L) or PCA measurement
Dependent identifier Information (cp: Dose or PK, pca: PCA, factor)
Event identifier
Weight (kg)
Age (yr)
Sex (male or female, factor)
Funaki T, Holford N, Fujita S (2018). Population PKPD analysis using nlmixr2 and NONMEM. PAGJA 2018
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.
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
Subject ID
Time (hrs)
Rate
Dose Amount Keyword
Dependent Variable, WBC
Compartment Number
Input Peripheral Volume
Input Central Volume
Input Clearance
nlmixr2/rxode2 classic evid
wbcSimwbcSim
An object of class data.frame with 280 rows and 10 columns.
Simulated Data for WBC pac ddmore model
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