Modeling & Simulation in R
Preface
Who this book is for
How to read this book
I Introduction
1
Quick Installation Guide
1.1
Windows
1.2
Linux specific: If you use Microsoft Open R
1.3
Linux (not REDHAT6 and CENTOS6) / Mac
1.4
Linux (REDHAT6 and CENTOS6)
1.5
NONMEM and MONOLIX
1.5.1
Windows
1.5.2
Unix
2
Installation
2.1
Requirements
2.2
Rtools on Windows
2.3
IQR Tools
2.3.1
Newest Version
2.3.2
Redhat 6 / Centos 6
2.3.3
Defined Version
2.4
Setup after installation
2.5
Activation
2.6
IQReport
2.7
IQRsbml
3
Examples in this Book
4
Reproducibility of Results
4.1
The CRAN Nightmare
4.2
MRAN Time Machine
4.3
IQR Tools impact
4.4
IQR Tools testing
4.4.1
Defining R and IQR Tools version
4.5
For the paranoid
5
Validation
5.1
Validation of IQR Tools
5.2
Validation support
6
Release Notes
6.1
V1.5.0 October 23, 2020
6.2
V1.4.0 August 21, 2020
6.3
V1.3.2 July 5, 2020
6.4
V1.3.1 May 20, 2020
6.5
V1.3.0 May 01, 2020
6.6
V1.2.1 March 08, 2020
6.7
V1.2.0 March 02, 2020
6.8
V1.1.1 January 31, 2020 (Brexit Day)
6.9
V1.1.0 December 23, 2019
6.10
V1.0.9 October 13, 2019
6.11
V1.0.8 September 13, 2019
6.12
V1.0.7 September 2, 2019
6.13
V1.0.6 May 28, 2019
6.14
V1.0.5 May 20, 2019
6.15
V1.0.4 April 19, 2019
6.16
V1.0.3 April 3, 2019
6.17
V1.0.2 March 18, 2019
6.18
V1.0.1 February 23, 2019
6.19
V1.0.0 February 8, 2019
6.20
V0.9.999 January 16, 2019
6.21
V0.9.99 December 06, 2018
6.22
V0.9.9 November 27, 2018
6.23
V0.9.3 October 25, 2018
6.24
V0.9.2 October 18, 2018
6.25
V0.9.1 October 07, 2018
6.26
V0.9.0 September 03, 2018
6.27
V0.8.1 August 7, 2018
6.28
V0.8.0 June 25, 2018
6.29
V0.7.2 May 10, 2018
6.30
V0.7.0 April 23, 2018
6.31
V0.6.6 April 16, 2018
6.32
V0.6.4 March 16, 2018
6.33
V0.6.3 March 08, 2018
6.34
V0.6.2 February 08, 2018
6.35
V0.6.1 January 26, 2018
6.36
V0.6.0 January 24, 2018
6.37
V0.5.8 January 19, 2018
6.38
V0.5.7 January 16, 2018
6.39
V0.5.6 December 14, 2017
6.40
V0.5.5 December 04, 2017
6.41
V0.5.1 October 14, 2017
6.42
V0.5.0 October 10, 2017
6.43
V0.4.2 September 21, 2017
6.44
V0.4.1 September 14, 2017
6.45
V0.3.0 August 14, 2017
6.46
V0.2.0 July 14, 2017
6.47
V0.1.0 June 5, 2017
II Case Studies
7
Analysis dataset preparation
7.1
Example workflow
7.1.1
Original dataset in general row-based format
7.1.2
Import as IQRdataGENERAL format
7.1.3
Source data exploration
7.1.4
Cleaning to create an analysis dataset
7.1.5
Export
7.2
Workflow customization
7.2.1
Dataset handling
7.2.2
Import/export options
7.2.3
Cleaning options
7.2.4
Data exploration
8
Model definition
8.1
Model definition basics
8.1.1
Biochemical reaction model
8.1.2
One compartment linear PK
8.1.3
PK/PD
8.2
Dosing representation
8.3
Advanced model definition
8.3.1
Example
8.3.2
Lagtimes
8.3.3
Mathematical functions
8.3.4
Implementing conditional statements (
if-then-else
)
8.3.5
Interpolation functions
8.3.6
MODEL FUNCTIONS section
8.3.7
MODEL EVENTS section
8.4
Handling of models in R
8.4.1
Model import
8.4.2
Support of SBML
8.4.3
Basic model information
8.4.4
Model export
8.5
PK model library
8.6
Example models
8.6.1
PBPK
8.6.2
Friberg neutropenia
8.6.3
Novak-Tyson Cell-Cycle
8.6.4
Parasitemia PK/PD
8.6.5
Bouncing ball
8.6.6
C-Functions
8.6.7
Fantasy events
8.6.8
Novak-Tyson biochemical
9
Simulation of models
9.1
Simulation
9.2
Simulation settings
9.2.1
Simulation time
9.2.2
Initial conditions
9.2.3
Parameters
9.2.4
Output selection
9.3
Dosing events
9.3.1
Single dosing input
9.3.2
Multiple dosing inputs
9.3.3
Special dosing parameters
9.4
Parameter sensitivities
9.5
Integrator in C
10
NLME Modeling
10.1
Longitudinal Models
10.1.1
Required data format
10.1.2
Structural models
10.1.3
Linear vs.
nonlinear models
10.1.4
Time varying covariates
10.1.5
Basic PK model
10.1.6
Tabular results
10.1.7
General diagnostics
10.1.8
Output diagnostics
10.1.9
Lagtime and FOCEI
10.1.10
Zero-order absorption
10.1.11
NLME model settings
10.1.12
Sequential PK/PD
10.1.13
Regression parameters
10.1.14
Error models
10.1.15
Multiple outputs
10.1.16
Basic covariate models
10.1.17
Covariate plots
10.1.18
Complex covariates
10.1.19
Covariance
10.1.20
BLOQ data
10.1.21
IV/SC PK model
10.1.22
NONMEM Bayes
10.1.23
Other features
10.2
Time-to-event models
10.2.1
Data format
10.2.2
Defining TTE NLME models
10.2.3
Weibull
10.2.4
Weibull with delay
10.2.5
Exponential
10.2.6
Exponential with delay
10.2.7
Gompertz
10.2.8
Gompertz with delay
10.2.9
Log-logistic
10.2.10
Diagnostics
10.3
Joint models
10.3.1
Longitudinal + TTE
10.3.2
Data format
10.3.3
RTTE & interval censoring
10.3.4
NONMEM
11
QSP Modeling
11.1
Background
11.2
Interface
11.2.1
Data
11.2.2
ModelSpec
11.3
Systems Biology Example: Epo-Receptor
11.3.1
Basic model simulation
11.3.2
Manipulating parameters for simulations
11.3.3
Defining experimental conditions
11.3.4
Modeling data - exploration by manual parameter tweaking
11.3.5
Modeling data - parameter estimation
11.3.6
Modeling data - multistart optimization
11.3.7
Modeling data - Profile Likelihood
11.3.8
Modelling data - IIV and BLOQ (censored data)
12
Model evaluation
12.1
Goodness-of-fit
12.1.1
Random effects
12.1.2
Random effects / covariates
12.1.3
GOF plots
12.1.4
Individual plots
12.1.5
Export to file
12.1.6
Plot data
12.2
VPC
12.2.1
Generate VPC
12.2.2
Prediction corrected VPC (pcVPC)
12.2.3
VPC data
12.2.4
VPC sequential modeling
12.2.5
Additional settings
12.3
Bootstrap
12.3.1
Generate bootstrap
12.3.2
Bootstrap results
12.3.3
Stratification
12.3.4
Large bootstraps
12.4
Profile likelihood
13
Advanced modeling workflows
13.1
PopPK workflow
13.2
Covariate selection
13.3
Pop PK/PD workflow
14
Population simulations
14.1
Basic population simulation
14.1.1
Basic example
14.1.2
Event table generation
14.1.3
Parameter sampling
14.1.4
Customizing simulations
14.2
Clinical trial simulation
14.2.1
Parallel design example
14.2.2
Adaptive design example
15
Experimental design
15.1
Use of PopED
15.1.1
PopED / IQR Toolsinterface
15.1.2
Same example in PopED
15.2
Use of profile likelihood
16
Exposure response analysis
16.1
Logistic regression
16.1.1
Single predictor
16.1.2
Multiple predictors
16.2
Kaplan-Meier
16.2.1
Simple plot
16.2.2
Stratified plot
16.2.3
Style and annotation
16.2.4
Risk table
16.2.5
Confidence intervals
16.2.6
Using the CENScol argument
16.3
Cox Regression
17
Reporting in Microsoft Word
17.1
Example analysis report
17.2
Applying styles when creating Word document
III Manuals
18
General Dataset Format
18.1
General columns
18.2
Additional columns
18.3
Deprecated columns
19
Structural Model Syntax
19.1
Model sections
19.1.1
Model name
19.1.2
Model notes
19.1.3
Model states
19.1.4
Model state information
19.1.5
Model parameters
19.1.6
Model variables
19.1.7
Model reactions
19.1.8
Model functions
19.1.9
Model events
19.1.10
Model C functions
19.2
Pre-defined functions
19.3
IQRmodel object
20
Dosing definition
20.1
IQRdosing object
21
General Parameter Format (GPF)
21.1
The GPF excel file
21.2
Columns in the GPF estimates sheet
21.2.1
Naming convention
21.2.2
Example GPF file
21.3
Parameter transformations
21.3.1
Transformation between original and normal units
21.4
Basic terms
22
Random sampling of NLME model parameters
22.1
Input
22.1.1
Example GPF file
22.1.2
Example patient data
22.2
Calling the function
sampleIndParamValues
22.2.1
Output
22.2.2
Example output
22.3
Covariate adjustment formulae
22.4
Possible values for FLAG_SAMPLE
22.5
Sampling steps
22.5.1
Step 1: Sampling of population parameter values
22.5.2
Step 2: Sampling records from the patient data
22.5.3
Step 3: Calculating typical individual parameter values
22.5.4
Step 4: Sampling random effects
22.5.5
Step 5: Calculating individual parameter values
22.6
Testing for sampling discrepancies
22.6.1
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 generating and I/O
A.7
GPF helpers
A.8
Help & Documentation
A.9
IQR Table
A.10
IQR plot
A.11
IQR report
A.12
IQR shiny
A.13
IQR slides
A.14
IQRdataER
A.15
IQRdataGENERAL
A.16
IQRdataGENERAL auxiliaries
A.17
IQRdataGeneral
A.18
IQRdataNLME
A.19
IQRmodel
A.20
IQRnlmeData
A.21
IQRnlmeProject
A.22
IQRtteProject
A.23
Installation
A.24
Logistic regression
A.25
MONOLIX
A.26
NCA
A.27
NLMIXR
A.28
Optimal Experiment Design
A.29
Output
A.30
Output & Compliance
A.31
QSP
A.32
SAS
A.33
Sensitivity Analysis
A.34
Simulation
A.35
Statistics
A.36
Submission package
A.37
Survival analysis
A.38
VPC
A.39
dMod interface
A.40
functions in the NLME parameter sampling API
A.41
stat functions
(c) 2018-2020 IntiQuan GmbH
Modeling & Simulation in R
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