Introduction to Population Pharmacokinetic / Pharmacodynamic Analysis with Nonlinear Mixed Effects Models

Name: Introduction to Population Pharmacokinetic / Pharmacodynamic Analysis with Nonlinear Mixed Effects Models
Brand: Wiley
Price: 102.99 EUR
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Joel S. Owen Jill Fiedler-Kelly(Autor*in)

Wiley (Verlag)

Erschienen am 18. Juni 2014

320 Seiten

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Inhalt

Intro
Introduction to Population Pharmacokinetic/Pharmacodynaic Analysis with Nonlinear Mixed Effects Models
Copyright
Contents
Preface
CHAPTER 1 The Practice of Pharmacometrics
1.1 Introduction
1.2 Applications of Sparse Data Analysis
1.3 Impact of Pharmacometrics
1.4 Clinical Example
References
CHAPTER 2 Population Model Concepts and Terminology
2.1 Introduction
2.2 Model elements
2.3 Individual Subject Models
2.4 Population Models
2.4.1 Fixed-Effect Parameters
2.4.2 Random-Effect Parameters
2.5 Models of Random Between-Subject Variability (L1)
2.5.1 Additive Variation
2.5.2 Constant Coefficient of Variation
2.5.3 Exponential Variation
2.5.4 Modeling Sources of Between-Subject Variation
2.6 Models of Random Variability in Observations (L2)
2.6.1 Additive Variation
2.6.2 Constant Coefficient of Variation
2.6.3 Additive Plus CCV Model
2.6.4 Log-Error Model
2.6.5 Relationship Between RV Expressions and Predicted Concentrations
2.6.6 Significance of the Magnitude of RV
2.7 Estimation Methods
2.8 Objective Function
2.9 Bayesian Estimation
References
CHAPTER 3 NONMEM Overview and Writing an NM-TRAN Control Stream
3.1 Introduction
3.2 Components of the NONMEM System
3.3 General Rules
3.4 Required Control Stream Components
3.4.1 $PROBLEM Record
3.4.2 The $DATA Record
3.4.3 The $INPUT Record
3.5 Specifying the Model in NM-TRAN
3.5.1 Calling PREDPP Subroutines for Specific PK Models
3.5.2 Specifying the Model in the $PK Block
3.5.3 Specifying Residual Variability in the $ERROR Block
3.5.4 Specifying Models Using the $PRED Block
3.6 Specifying Initial Estimates with $THETA, $OMEGA, and $SIGMA
3.7 Requesting Estimation and Related Options
3.8 Requesting Estimates of the Precision of Parameter Estimates
3.9 Controlling the Output
References
CHAPTER 4 Datasets
4.1 Introduction
4.2 Arrangement of the Dataset
4.3 Variables of the Dataset
4.3.1 TIME
4.3.2 DATE
4.3.3 ID
4.3.4 DV
4.3.5 MDV
4.3.6 CMT
4.3.7 EVID
4.3.8 AMT
4.3.9 RATE
4.3.10 ADDL
4.3.11 II
4.3.12 SS
4.4 Constructing Datasets with Flexibility to Apply Alternate Models
4.5 Examples of Event Records
4.5.1 Alternatives for Specifying Time
4.5.2 Infusions and Zero-Order Input
4.5.3 Using ADDL
4.5.4 Steady-State Approach
4.5.5 Samples Before and After Achieving Steady State
4.5.6 Unscheduled Doses in a Steady-State Regimen
4.5.7 Steady-State Dosing with an Irregular Dosing Interval
4.5.8 Multiple Routes of Administration
4.5.9 Modeling Multiple Dependent Variable Data Types
4.5.10 Dataset for $PRED
4.6 Beyond Doses and Observations
4.6.1 Other Data Items
4.6.2 Covariate Changes over Time
4.6.3 Inclusion of a Header Row
References
CHAPTER 5 Model Building: Typical Process
5.1 Introduction
5.2 Analysis Planning
5.3 Analysis Dataset Creation
5.4 Dataset Quality Control
5.5 Exploratory Data Analysis
5.5.1 EDA: Population Description
5.5.2 EDA: Dose-Related Data
5.5.3 EDA: Concentration-Related Data
5.5.4 EDA: Considerations with Large Datasets
5.5.5 EDA: Summary
5.6 Base Model Development
5.6.1 Standard Model Diagnostic Plots and Interpretation
5.6.2 Estimation of Random Effects
5.6.3 Precision of Parameter Estimates (Based on $COV Step)
5.7 Covariate Evaluation
5.7.1 Covariate Evaluation Methodologies
5.7.2 Statistical Basis for Covariate Selection
5.7.3 Diagnostic Plots to Illustrate Parameter-Covariate Relationships
5.7.4 Typical Functional Forms for Covariate-Parameter Relationships
5.7.5 Centering Covariate Effects
5.7.6 Forward Selection Process
5.7.7 Evaluation of the Full Multivariable Model
5.7.8 Backward Elimination Process
5.7.9 Other Covariate Evaluation Approaches
5.8 Model Refinement
Appendix 5.1 Recommended Analysis Plan Content
References
CHAPTER 6 Interpreting the NONMEM Output
6.1 Introduction
6.2 Description of the Output Files
6.3 The NONMEM Report File
6.3.1 NONMEM-Related Output
6.3.2 PREDPP-Related Output
6.3.3 Output from Monitoring of the Search
6.3.4 Minimum Value of the Objective Function and Final Parameter Estimates
6.3.5 Covariance Step Output
6.3.6 Additional Output
6.4 Error Messages: Interpretation and Resolution
6.4.1 NM-TRAN Errors
6.4.2 $ESTIMATION Step Failures
6.4.3 $COVARIANCE Step Failures
6.4.4 PREDPP Errors
6.4.5 Other Types of NONMEM Errors
6.4.6 fortran compiler or other run-time errors
6.5 general suggestIons for dIagnosIng probleMs
Appendix 6.1 Mixture Model Example Code
References
CHAPTER 7 App lications Using Parameter Estimates from the Individual
7.1 Introduction
7.2 Bayes Theorem and Individual Parameter Estimates
7.3 Obtaining Individual Parameter Estimates
7.4 Applications of Individual Parameter Estimates
7.4.1 Generating Subject-Specific Exposure Estimates
7.4.2 Individual Exposure Estimates for Group Comparisons
References
CHAPTER 8 Introduction to Model Evaluation
8.1 Introduction
8.2 Internal Validation
8.3 External Validation
8.4 Predictive Performance Assessment
8.5 Objective Function Mapping
8.6 Leverage Analysis
8.7 Bootstrap Procedures
8.8 Visual and Numerical Predictive Check Procedures
8.8.1 The VPC Procedure
8.8.2 Presentation of VPC Results
8.8.3 The Numerical Predictive Check (NPC) Procedure
8.9 Posterior Predictive Check Procedures
References
CHAPTER 9 User-Written Models
9.1 Introduction
9.2 $MODEL
9.3 $SUBROUTINES
9.3.1 General Linear Models (ADVAN5 and ADVAN7)
9.3.2 General Nonlinear Models (ADVAN6, ADVAN8, ADVAN9, and ADVAN13)
9.3.3 $DES
9.4 A Series of Examples
9.4.1 Defined Fractions Absorbed by Zero- and First-Order Processes
9.4.2 Sequential Absorption with First-Order Rates, without Defined Fractions
9.4.3 Parallel Zero-Order and First-Order Absorption, without Defined Fractions
9.4.4 Parallel First-Order Absorption Processes, without Defined Fractions
9.4.5 Zero-Order Input into the Depot Compartment
9.4.6 Parent and Metabolite Model: Differential Equations
References
CHAPTER 10 PK/PD Models
10.1 Introduction
10.2 Implementation of PD Models in NONMEM
10.3 $PRED
10.3.1 Direct-Effect PK/PD Examples: PK Concentrations in the Dataset
10.3.2 Direct-Effect PK/PD Example: PK from Computed Concentrations
10.4 $PK
10.4.1 Specific ADVANs (ADVAN1-ADVAN4 and ADVAN10-ADVAN12)
10.4.2 General ADVANs (ADVAN5-ADVAN9 and ADVAN13)
10.4.3 PREDPP: Effect Compartment Link Model Example (PD in $ERROR)
10.4.4 PREDPP: Indirect Response Model Example: PD in $DES
10.5 Odd-Type Data: Analysis of Noncontinuous Data
10.6 PD Model Complexity
10.7 Communication of Results
References
CHAPTER 11 Simulation Basics
11.1 Introduction
11.2 The Simulation Plan
11.2.1 Simulation Components
11.2.2 The Input-Output Model
11.2.3 The Covariate Distribution Model
11.2.4 The Trial Execution Model
11.2.5 Replication of the Study
11.2.6 Analysis of the Simulated Data
11.2.7 Decision Making Using Simulations
11.3 Miscellaneous Other Simulation-Related Considerations
11.3.1 The Seed Value
11.3.2 Consideration of Parameter Uncertainty
11.3.3 Constraining Random Effects or Responses
Appendix 11.1 Example Simulation Output
Appendix 11.2 Example Simulation Output with a Different Seed Value
Appendix 11.3 Simulation with Parameter Uncertainty
References
CHAPTER 12 Quality Control
12.1 Introduction
12.2 QC of the Data Analysis Plan
12.3 Analysis Dataset Creation
12.3.1 Exploratory Data Analysis and Its Role in Dataset QC
12.3.2 QC in Data Collection
12.4 QC of Model Development
12.4.1 QC of NM-TRAN Control Streams
12.4.2 Model diagnostic Plots and Model evaluation steps as Qc
12.5 Documentation of QC Efforts
12.6 Summary
References
Index

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