Pharmacometrics with NONMEM and R

책 소개

This book follows the practice of population pharmacokinetic and pharmacodynamic analysis,

from repeated model fits to the choice of a final model and the decisions it supports.

Volume 2, Pharmacokinetics with R, examined how concentrations and effects change over

time. Here those models are fitted to observed data and used to address practical questions.

I have taught NONMEM® since 1999 and used it for longer. Several difficulties recur:

carriage-control information in the first output column, the scale used for SIGDIG, interpretation

of a condition-number threshold of 1000, and body weight recorded in VS rather than

DM. The examples examine these issues and their consequences.

Four principles guide the examples.

• Learn from analysis failures. I reconstruct failures from my own work and describe

checks that could have prevented them. Other examples include a dose transcription error

that has persisted for thirty years in a widely used teaching dataset.

• Test the checks. A check must be shown to reject incorrect input before a passing result

can be interpreted as evidence.

• Revisit earlier decisions. Residual diagnostics and VPCs can lead back to data preparation.

The examples retain these iterations rather than presenting only the final sequence

of steps.

• Verify by measurement. RAM-disk performance and sensitivity of the condition number

to starting values are measured directly. Calculations obtained from other sources

are also repeated.

Scripts in the repository (https://github.com/AMC-CPT/PMx) generated the numerical results

in the text, with the reproducibility exception described below. Running the NONMEM

exercises requires a license. The exercises involve editing a control stream and rerunning

NONMEM to examine the result. Supplied NONMEM outputs allow readers without a license

to run the R code, build the book and check the printed calculations; they do not replace

executing NONMEM when working through those exercises.

The examples use public or simulated data except for the second monkey example in

Chapter 18. Those data came from a practical analysis, with the development code and

animal identifiers removed. The public repository provides simulated data under the same

name, so its results for that section are similar to, but not identical with, the printed results.

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목차

Preface .............................................................................. iii

I Preparation .......................................................................... 1

1 Pharmacometrics in Decision Making ............................................... 3

1.1 The Role of Models in Decision Making ...................................... 3

1.2 Historical Development ..................................................... 4

1.3 Prespecification in the Analysis Plan ...................................... 7

1.4 Organization of This Book .................................................. 7

Exercises ..................................................................... 11

References .................................................................... 12

2 NONMEM Installation and Output Processing ....................................... 13

2.1 Installation Requirements ................................................. 13

2.2 Execution Through a Shell Wrapper ......................................... 14

2.3 Carriage-Control Information in the First Output Column ................... 15

2.4 TrimOut ................................................................... 16

2.5 The Need for Output Processing ............................................ 17

2.6 What Changed between 7.4 and 7.6 .......................................... 18

2.7 Other Available Tools ..................................................... 19

Exercises ..................................................................... 21

3 Working Environment and Reproducibility ......................................... 23

3.1 Folder Conventions ........................................................ 23

3.2 Records Required to Reproduce Random Numbers .............................. 24

3.3 Detecting Input Changes with Hashes ....................................... 25

3.4 Automation and Optional Processing ........................................ 26

3.5 Implementation of the Conventions in the Repository ....................... 27

3.6 IQ, OQ, PQ ................................................................ 27

Exercises ..................................................................... 29

4 Writing Control Streams ......................................................... 31

4.1 The Order of the Records .................................................. 31

4.2 $PROB: Recording the Model Lineage in the File ............................ 32

4.3 The Two Records That Read the Data ........................................ 33

4.4 The Two Records That Specify the Model .................................... 33

4.5 $PK: Structure and Covariates ............................................. 35

4.6 $ERROR: Combining the Residual Error in W ................................. 36

4.7 Initial Estimates and Bounds .............................................. 37

4.8 Estimation and Output ..................................................... 39

4.9 A Convention for the EM Family: MU Referencing ............................ 40

4.10 Complete Control Stream .................................................. 41

4.11 Rules for Handling Files ................................................. 42

Exercises ..................................................................... 43

II Data ............................................................................... 45

5 Data Assembly: From Domains to a NONMEM Dataset ................................. 47

5.1 Source Data Domains ....................................................... 47

5.2 The Practice Data of This Chapter ......................................... 49

5.3 Domain-Specific Data Processing ........................................... 50

5.4 Handling Missing Values ................................................... 58

5.5 Merging Data Domains ...................................................... 60

5.6 Verification Against Reference Data ....................................... 63

5.7 Setting the Conventions ................................................... 65

5.8 Pooling Several Studies ................................................... 65

5.9 Required Data-Preparation Deliverables .................................... 66

Exercises ..................................................................... 67

6 Data Checking and Verification .................................................. 69

6.1 Verification of Dose Calculations in the THEO Data ........................ 69

6.2 Initial Data Reconciliation ............................................... 71

6.3 Detecting Ineffective Validation Checks ................................... 72

6.4 Verification of Record Order .............................................. 73

6.5 Bundling the Checks ....................................................... 75

6.6 Validation with Deliberately Incorrect Inputs ............................. 76

6.7 Deriving the Coding from the Data ......................................... 78

6.8 Distinguishing the Meanings of Zero Values ................................ 79

6.9 Documentation and Resolution of Findings .................................. 84

Exercises ..................................................................... 85

References .................................................................... 86

III Model Building .................................................................... 87

7 The Base Model .................................................................. 89

7.1 Decisions Before Model Fitting ............................................ 89

7.2 Verification of the Data Input ............................................ 90

7.3 The First Fit ............................................................. 91

7.4 Correlation Structure of the Interindividual Variability .................. 91

7.5 Reconsideration of Model Complexity ....................................... 93

7.6 Goodness-of-Fit Plots .....................................

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