Preface
Who this book is for
How to read this book
I Introduction
1
License and Availability
1.1
Source Code
2
Requirements
3
Installation
3.1
IQR Tools
3.2
Setup after installation
4
Examples in this Book
4.1
Public version IQR Tools
4.2
Versioned IQR Tools
5
Reproducibility of Results
5.1
The CRAN Nightmare
5.2
MRAN Time Machine
5.3
IQR Tools Installer
6
Validation
6.1
Validation of IQR Tools
6.2
Validation support
7
Release Notes
7.1
PUBLIC Version of IQR Tools
7.2
V1.17.0 December, 2023
7.3
V1.16.0 July, 2023
7.4
V1.15.0 June, 2023
7.5
V1.14.0 April, 2023
7.6
V1.12.0 November, 2022
7.7
V1.11.0 October, 2022
7.8
V1.10.0 May, 2022
7.9
V1.9.0 February 01, 2022
7.10
V1.8.0 October 16, 2021
7.11
V1.7.2 September 15, 2021
7.12
V1.7.1 May 4, 2021
7.13
V1.7.0 April 10, 2021
7.14
V1.6.0 February 18, 2021
7.15
V1.5.0 October 23, 2020
7.16
V1.4.0 August 21, 2020
7.17
V1.3.2 July 5, 2020
7.18
V1.3.1 May 20, 2020
7.19
V1.3.0 May 01, 2020
7.20
V1.2.1 March 08, 2020
7.21
V1.2.0 March 02, 2020
7.22
V1.1.1 January 31, 2020 (Brexit Day)
7.23
V1.1.0 December 23, 2019
7.24
V1.0.9 October 13, 2019
7.25
V1.0.8 September 13, 2019
7.26
V1.0.7 September 2, 2019
7.27
V1.0.6 May 28, 2019
7.28
V1.0.5 May 20, 2019
7.29
V1.0.4 April 19, 2019
7.30
V1.0.3 April 3, 2019
7.31
V1.0.2 March 18, 2019
7.32
V1.0.1 February 23, 2019
7.33
V1.0.0 February 8, 2019
7.34
V0.9.999 January 16, 2019
7.35
V0.9.99 December 06, 2018
7.36
V0.9.9 November 27, 2018
7.37
V0.9.3 October 25, 2018
7.38
V0.9.2 October 18, 2018
7.39
V0.9.1 October 07, 2018
7.40
V0.9.0 September 03, 2018
7.41
V0.8.1 August 7, 2018
7.42
V0.8.0 June 25, 2018
7.43
V0.7.2 May 10, 2018
7.44
V0.7.0 April 23, 2018
7.45
V0.6.6 April 16, 2018
7.46
V0.6.4 March 16, 2018
7.47
V0.6.3 March 08, 2018
7.48
V0.6.2 February 08, 2018
7.49
V0.6.1 January 26, 2018
7.50
V0.6.0 January 24, 2018
7.51
V0.5.8 January 19, 2018
7.52
V0.5.7 January 16, 2018
7.53
V0.5.6 December 14, 2017
7.54
V0.5.5 December 04, 2017
7.55
V0.5.1 October 14, 2017
7.56
V0.5.0 October 10, 2017
7.57
V0.4.2 September 21, 2017
7.58
V0.4.1 September 14, 2017
7.59
V0.3.0 August 14, 2017
7.60
V0.2.0 July 14, 2017
7.61
V0.1.0 June 5, 2017
II Case Studies
8
Analysis dataset preparation
8.1
Example workflow
8.1.1
Original dataset in general row-based format
8.1.2
Import as IQRdataGENERAL format
8.1.3
Source data exploration
8.1.4
Cleaning to create an analysis dataset
8.1.5
Export
8.2
Workflow customization
8.2.1
Dataset handling
8.2.2
Import/export options
8.2.3
Cleaning options
8.2.4
Data exploration
9
Model definition
9.1
Model definition basics
9.1.1
Biochemical reaction model
9.1.2
One compartment linear PK
9.1.3
PK/PD
9.2
Dosing representation
9.3
Advanced model definition
9.3.1
Example
9.3.2
Lagtimes
9.3.3
Mathematical functions
9.3.4
Implementing conditional statements (
if-then-else
)
9.3.5
Interpolation functions
9.3.6
MODEL FUNCTIONS section
9.3.7
MODEL EVENTS section
9.4
Handling of models in R
9.4.1
Model import
9.4.2
Support of SBML
9.4.3
Basic model information
9.4.4
Model export
9.5
PK model library
9.6
Example models
9.6.1
PBPK
9.6.2
Friberg neutropenia
9.6.3
Novak-Tyson Cell-Cycle
9.6.4
Parasitemia PK/PD
9.6.5
Bouncing ball
9.6.6
C-Functions
9.6.7
Fantasy events
9.6.8
Novak-Tyson biochemical
10
Simulation of models
10.1
Simulation
10.2
Simulation settings
10.2.1
Simulation time
10.2.2
Initial conditions
10.2.3
Parameters
10.2.4
Output selection
10.3
Dosing events
10.3.1
Single dosing input
10.3.2
Multiple dosing inputs
10.3.3
Special dosing parameters
10.4
Parameter sensitivities
10.5
Integrator in C
11
NLME Modeling
11.1
Longitudinal Models
11.1.1
Required data format
11.1.2
Structural models
11.1.3
Linear vs. nonlinear models
11.1.4
Time varying covariates
11.1.5
Basic PK model
11.1.6
Tabular results
11.1.7
General diagnostics
11.1.8
Output diagnostics
11.1.9
Lagtime and FOCEI
11.1.10
Zero-order absorption
11.1.11
NLME model settings
11.1.12
Sequential PK/PD
11.1.13
Regression parameters
11.1.14
Error models
11.1.15
Multiple outputs
11.1.16
Basic covariate models
11.1.17
Covariate plots
11.1.18
Complex covariates
11.1.19
Covariance
11.1.20
BLOQ data
11.1.21
IV/SC PK model
11.1.22
NONMEM Bayes
11.1.23
Other features
11.2
Time-to-event models
11.2.1
Data format
11.2.2
Defining TTE NLME models
11.2.3
Weibull
11.2.4
Weibull with delay
11.2.5
Exponential
11.2.6
Exponential with delay
11.2.7
Gompertz
11.2.8
Gompertz with delay
11.2.9
Log-logistic
11.2.10
Diagnostics
11.3
Joint models
11.3.1
Longitudinal + TTE
11.3.2
Data format
11.3.3
RTTE & interval censoring
11.3.4
NONMEM
12
QSP Modeling
12.1
Background
12.2
Interface
12.2.1
Data
12.2.2
ModelSpec
12.3
Systems Biology Example: Epo-Receptor
12.3.1
Basic model simulation
12.3.2
Manipulating parameters for simulations
12.3.3
Defining experimental conditions
12.3.4
Modeling data - exploration by manual parameter tweaking
12.3.5
Modeling data - parameter estimation
12.3.6
Modeling data - multistart optimization
12.3.7
Modeling data - Profile Likelihood
12.3.8
Modelling data - IIV and BLOQ (censored data)
13
Model evaluation
13.1
Goodness-of-fit
13.1.1
Random effects
13.1.2
Random effects / covariates
13.1.3
GOF plots
13.1.4
Individual plots
13.1.5
Export to file
13.1.6
Plot data
13.2
VPC
13.2.1
Generate VPC
13.2.2
Prediction corrected VPC (pcVPC)
13.2.3
VPC data
13.2.4
VPC sequential modeling
13.2.5
Additional settings
13.3
Bootstrap
13.3.1
Generate bootstrap
13.3.2
Bootstrap results
13.3.3
Stratification
13.3.4
Large bootstraps
13.4
Profile likelihood
14
Advanced modeling workflows
14.1
PopPK workflow
14.2
Covariate selection
14.3
Pop PK/PD workflow
15
Population simulations
15.1
Basic population simulation
15.1.1
Basic example
15.1.2
Event table generation
15.1.3
Parameter sampling
15.1.4
Customizing simulations
15.2
Clinical trial simulation
15.2.1
Parallel design example
15.2.2
Adaptive design example
16
Experimental design
16.1
Use of PopED
16.1.1
PopED / IQR Toolsinterface
16.1.2
Same example in PopED
16.2
Use of profile likelihood
17
Exposure response analysis
17.1
Logistic regression
17.1.1
Single predictor
17.1.2
Multiple predictors
17.2
Kaplan-Meier
17.2.1
Simple plot
17.2.2
Stratified plot
17.2.3
Style and annotation
17.2.4
Risk table
17.2.5
Confidence intervals
17.2.6
Using the CENScol argument
17.3
Cox Regression
18
Reporting in Microsoft Word
18.1
Example analysis report
18.2
Applying styles when creating Word document
III Manuals
19
General Dataset Format
19.1
General columns
19.2
Additional columns
19.3
Deprecated columns
20
Structural Model Syntax
20.1
Model sections
20.1.1
Model name
20.1.2
Model notes
20.1.3
Model states
20.1.4
Model state information
20.1.5
Model parameters
20.1.6
Model variables
20.1.7
Model reactions
20.1.8
Model functions
20.1.9
Model events
20.1.10
Model C functions
20.2
Pre-defined functions
20.3
IQRmodel object
21
Dosing definition
21.1
IQRdosing object
22
General Parameter Format (GPF)
22.1
The GPF excel file
22.2
Columns in the GPF estimates sheet
22.2.1
Naming convention
22.2.2
Example GPF file
22.3
Parameter transformations
22.3.1
Transformation between original and normal units
22.4
Basic terms
23
Random sampling of NLME model parameters
23.1
Input
23.1.1
Example GPF file
23.1.2
Example patient data
23.2
Calling the function
sampleIndParamValues
23.2.1
Output
23.2.2
Example output
23.3
Covariate adjustment formulae
23.4
Possible values for FLAG_SAMPLE
23.5
Sampling steps
23.5.1
Step 1: Sampling of population parameter values
23.5.2
Step 2: Sampling records from the patient data
23.5.3
Step 3: Calculating typical individual parameter values
23.5.4
Step 4: Sampling random effects
23.5.5
Step 5: Calculating individual parameter values
23.6
Testing for sampling discrepancies
23.6.1
Detecting discrepancies in samples from the parameter uncertainty distribution
Appendix
A
Function Reference
A.1
Allowed in IQRmodel
A.2
Auxiliary
A.3
Bootstrap
A.4
Covariate analysis
A.5
Data handling
A.6
GPF
A.7
Help & Documentation
A.8
Installation
A.9
IQR plot
A.10
IQR report
A.11
IQR shiny
A.12
IQR slides
A.13
IQR Table
A.14
IQRaedataER
A.15
IQRdataER
A.16
IQRdataGeneral
A.17
IQRdataGENERAL
A.18
IQRdataGENERAL auxiliaries
A.19
IQRdataNLME
A.20
IQRmodel
A.21
IQRnlmeData
A.22
IQRnlmeProject
A.23
IQRtteProject
A.24
Logistic regression
A.25
MONOLIX
A.26
NLMIXR
A.27
Optimal Experiment Design
A.28
Output
A.29
Parameter sampling
A.30
QSP
A.31
SAS
A.32
Sensitivity Analysis
A.33
Simulation
A.34
stat functions
A.35
Statistics
A.36
Submission package
A.37
Survival analysis
A.38
VPC
(c) 2018-2023 IntiQuan AG
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