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link管理  ›  15 Population simulations | _main
table models
https://iqrtools.intiquan.com/doc/book/population-simulations.html
年轻有为的茴香
3 月前
  • 7.21 V1.2.1 March 08, 2020
  • 7.22 V1.2.0 March 02, 2020
  • 7.23 V1.1.1 January 31, 2020 (Brexit Day)
  • 7.24 V1.1.0 December 23, 2019
  • 7.25 V1.0.9 October 13, 2019
  • 7.26 V1.0.8 September 13, 2019
  • 7.27 V1.0.7 September 2, 2019
  • 7.28 V1.0.6 May 28, 2019
  • 7.29 V1.0.5 May 20, 2019
  • 7.30 V1.0.4 April 19, 2019
  • 7.31 V1.0.3 April 3, 2019
  • 7.32 V1.0.2 March 18, 2019
  • 7.33 V1.0.1 February 23, 2019
  • 7.34 V1.0.0 February 8, 2019
  • 7.35 V0.9.999 January 16, 2019
  • 7.36 V0.9.99 December 06, 2018
  • 7.37 V0.9.9 November 27, 2018
  • 7.38 V0.9.3 October 25, 2018
  • 7.39 V0.9.2 October 18, 2018
  • 7.40 V0.9.1 October 07, 2018
  • 7.41 V0.9.0 September 03, 2018
  • 7.42 V0.8.1 August 7, 2018
  • 7.43 V0.8.0 June 25, 2018
  • 7.44 V0.7.2 May 10, 2018
  • 7.45 V0.7.0 April 23, 2018
  • 7.46 V0.6.6 April 16, 2018
  • 7.47 V0.6.4 March 16, 2018
  • 7.48 V0.6.3 March 08, 2018
  • 7.49 V0.6.2 February 08, 2018
  • 7.50 V0.6.1 January 26, 2018
  • 7.51 V0.6.0 January 24, 2018
  • 7.52 V0.5.8 January 19, 2018
  • 7.53 V0.5.7 January 16, 2018
  • 7.54 V0.5.6 December 14, 2017
  • 7.55 V0.5.5 December 04, 2017
  • 7.56 V0.5.1 October 14, 2017
  • 7.57 V0.5.0 October 10, 2017
  • 7.58 V0.4.2 September 21, 2017
  • 7.59 V0.4.1 September 14, 2017
  • 7.60 V0.3.0 August 14, 2017
  • 7.61 V0.2.0 July 14, 2017
  • 7.62 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
    •  
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