uu.seUppsala University Publications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
dOFV distributions: A New Diagnostic For The Adequacy Of Parameter Uncertainty In Nonlinear Mixed-Effects Models Applied To The Bootstrap
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
2016 (English)In: Journal Of Pharmacokinetics And Pharmacodynamics, ISSN 1567-567X, Vol. 43, no 6, 597-608 p.Article in journal (Refereed) Published
Abstract [en]

Knowledge of the uncertainty in model parameters is essential for decision-making in drug development. Contrarily to other aspects of nonlinear mixed effects models (NLMEM), scrutiny towards assumptions around parameter uncertainty is low, and no diagnostic exists to judge whether the estimated uncertainty is appropriate. This work aims at introducing a diagnostic capable of assessing the appropriateness of a given parameter uncertainty distribution. The new diagnostic was applied to case bootstrap examples in order to investigate for which dataset sizes case bootstrap is appropriate for NLMEM. The proposed diagnostic is a plot comparing the distribution of differences in objective function values (dOFV) of the proposed uncertainty distribution to a theoretical Chi square distribution with degrees of freedom equal to the number of estimated model parameters. The uncertainty distribution was deemed appropriate if its dOFV distribution was overlaid with or below the theoretical distribution. The diagnostic was applied to the bootstrap of two real data and two simulated data examples, featuring pharmacokinetic and pharmacodynamic models and datasets of 20-200 individuals with between 2 and 5 observations on average per individual. In the real data examples, the diagnostic indicated that case bootstrap was unsuitable for NLMEM analyses with around 70 individuals. A measure of parameter-specific "effective" sample size was proposed as a potentially better indicator of bootstrap adequacy than overall sample size. In the simulation examples, bootstrap confidence intervals were shown to underestimate inter-individual variability at low sample sizes. The proposed diagnostic proved a relevant tool for assessing the appropriateness of a given parameter uncertainty distribution and as such it should be routinely used.

Place, publisher, year, edition, pages
2016. Vol. 43, no 6, 597-608 p.
Keyword [en]
Parameter uncertainty distributions, Bootstrap, Model diagnostics, Nonlinear mixed-effects models
National Category
Pharmaceutical Sciences
Identifiers
URN: urn:nbn:se:uu:diva-303628DOI: 10.1007/s10928-016-9496-7ISI: 000388634800004PubMedID: 27730481OAI: oai:DiVA.org:uu-303628DiVA: diva2:1038967
Funder
EU, FP7, Seventh Framework Programme, 115156
Available from: 2016-10-20 Created: 2016-09-21 Last updated: 2016-12-29Bibliographically approved
In thesis
1. Improved Methods for Pharmacometric Model-Based Decision-Making in Clinical Drug Development
Open this publication in new window or tab >>Improved Methods for Pharmacometric Model-Based Decision-Making in Clinical Drug Development
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Pharmacometric model-based analysis using nonlinear mixed-effects models (NLMEM) has to date mainly been applied to learning activities in drug development. However, such analyses can also serve as the primary analysis in confirmatory studies, which is expected to bring higher power than traditional analysis methods, among other advantages. Because of the high expertise in designing and interpreting confirmatory studies with other types of analyses and because of a number of unresolved uncertainties regarding the magnitude of potential gains and risks, pharmacometric analyses are traditionally not used as primary analysis in confirmatory trials.

The aim of this thesis was to address current hurdles hampering the use of pharmacometric model-based analysis in confirmatory settings by developing strategies to increase model compliance to distributional assumptions regarding the residual error, to improve the quantification of parameter uncertainty and to enable model prespecification.

A dynamic transform-both-sides approach capable of handling skewed and/or heteroscedastic residuals and a t-distribution approach allowing for symmetric heavy tails were developed and proved relevant tools to increase model compliance to distributional assumptions regarding the residual error. A diagnostic capable of assessing the appropriateness of parameter uncertainty distributions was developed, showing that currently used uncertainty methods such as bootstrap have limitations for NLMEM. A method based on sampling importance resampling (SIR) was thus proposed, which could provide parameter uncertainty in many situations where other methods fail such as with small datasets, highly nonlinear models or meta-analysis. SIR was successfully applied to predict the uncertainty in human plasma concentrations for the antibiotic colistin and its prodrug colistin methanesulfonate based on an interspecies whole-body physiologically based pharmacokinetic model. Lastly, strategies based on model-averaging were proposed to enable full model prespecification and proved to be valid alternatives to standard methodologies for studies assessing the QT prolongation potential of a drug and for phase III trials in rheumatoid arthritis.

In conclusion, improved methods for handling residual error, parameter uncertainty and model uncertainty in NLMEM were successfully developed. As confirmatory trials are among the most demanding in terms of patient-participation, cost and time in drug development, allowing (some of) these trials to be analyzed with pharmacometric model-based methods will help improve the safety and efficiency of drug development.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2016. 91 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 223
Keyword
pharmacometrics, nonlinear mixed-effects models, confirmatory trials, residual error modeling, parameter uncertainty, sampling importance resampling, model-averaging
National Category
Health Sciences
Research subject
Pharmaceutical Science
Identifiers
urn:nbn:se:uu:diva-305697 (URN)978-91-554-9734-7 (ISBN)
Public defence
2016-12-09, B/A1:107a, Biomedicinskt Centrum, Husargatan 3, Uppsala, 09:15 (English)
Opponent
Supervisors
Available from: 2016-11-18 Created: 2016-10-20 Last updated: 2016-11-28

Open Access in DiVA

fulltext(1072 kB)58 downloads
File information
File name FULLTEXT01.pdfFile size 1072 kBChecksum SHA-512
8dda124fccb78f70781ff4b9b4fd0dc26d19eddf24613ff0d63b00fd496d3dc69ace939ce71e3e81fd437285ab8a395b4d8645b0236afe3591987d5742382998
Type fulltextMimetype application/pdf

Other links

Publisher's full textPubMed

Search in DiVA

By author/editor
Dosne, Anne-GaëlleKarlsson, Mats O.
By organisation
Department of Pharmaceutical Biosciences
Pharmaceutical Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 58 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 270 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf