Methodology for Handling Missing Data in Nonlinear Mixed Effects Modelling

Methodology for Handling Missing Data in Nonlinear Mixed Effects Modelling

To obtain a better understanding of the pharmacokinetic and/or pharmacodynamic characteristics of an investigated treatment, clinical data is often analysed with nonlinear mixed effects modelling. The developed models can be used to design future clinical trials or to guide individualised drug treat...

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Bibliographic Details
Main Author: Johansson, Åsa M.
Format: Doctoral Thesis
Language:English
Published: Uppsala universitet, Institutionen för farmaceutisk biovetenskap 2014
Subjects:
Pharmacometrics
population models
censored observations
missing covariates
missing dependent variable
missing data mechanism
missing completely at random (MCAR)
missing at random (MAR)
missing not at random (MNAR)
estimation algorithms
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-224098
http://nbn-resolving.de/urn:isbn:978-91-554-8970-0

Internet

http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-224098
http://nbn-resolving.de/urn:isbn:978-91-554-8970-0

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