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The Impact of Censored Observations on Model Fit and Structural Model Discrimination in Nonlinear Mixed Effects Modelling when using Different Estimation Algorithms
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.
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Missing data due to censored observations is a common problem in nonlinear mixed effects modelling of clinical data. The aim of this study was to investigate how the estimated model parameters and the discrimination of correct structural model were affected by different patterns of censored observations and to investigate if there were any differences in these statistics when using different estimation algorithms to fit the models. Simulations generated data for 400 individuals with six observations per individual using a one-compartment model. Observations (62%) were censored according to three different missing data mechanisms. A one-compartment and a two-compartment model were fitted to the data using six different estimation algorithms.The performance of the algorithms was evaluated in a stochastic simulations and estimations study where 200 data sets were simulated. The algorithms were compared according to bias and precision of parameter estimates and according to the type I error rate in the evaluation of structural model. The EM algorithms, especially the importance sampling algorithms (IMP and IMPMAP), gave unbiased and precise parameter estimates as long as data were missing completely at random or missing at random, while the gradient based algorithms (especially FO and FOCE) experienced some problems with biased estimates under these missing data mechanisms. The type I error rate was not elevated when using any of the algorithms as long as the missing data mechanism was not missing not at random.

Keyword [en]
missing data, missing dependent variable, missing completely at random (MCAR), missing at random (MAR), missing not at random (MNAR), bias, precision, type I error
National Category
Pharmaceutical Sciences
Identifiers
URN: urn:nbn:se:uu:diva-224097OAI: oai:DiVA.org:uu-224097DiVA: diva2:715318
Available from: 2014-05-03 Created: 2014-05-03 Last updated: 2014-06-30
In thesis
1. Methodology for Handling Missing Data in Nonlinear Mixed Effects Modelling
Open this publication in new window or tab >>Methodology for Handling Missing Data in Nonlinear Mixed Effects Modelling
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

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 treatment. Missing data is a frequently encountered problem in analyses of clinical data, and to not venture the predictability of the developed model, it is of great importance that the method chosen to handle the missing data is adequate for its purpose. The overall aim of this thesis was to develop methods for handling missing data in the context of nonlinear mixed effects models and to compare strategies for handling missing data in order to provide guidance for efficient handling and consequences of inappropriate handling of missing data.

In accordance with missing data theory, all missing data can be divided into three categories; missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR). When data are MCAR, the underlying missing data mechanism does not depend on any observed or unobserved data; when data are MAR, the underlying missing data mechanism depends on observed data but not on unobserved data; when data are MNAR, the underlying missing data mechanism depends on the unobserved data itself.

Strategies and methods for handling missing observation data and missing covariate data were evaluated. These evaluations showed that the most frequently used estimation algorithm in nonlinear mixed effects modelling (first-order conditional estimation), resulted in biased parameter estimates independent on missing data mechanism. However, expectation maximization (EM) algorithms (e.g. importance sampling) resulted in unbiased and precise parameter estimates as long as data were MCAR or MAR. When the observation data are MNAR, a proper method for handling the missing data has to be applied to obtain unbiased and precise parameter estimates, independent on estimation algorithm.

The evaluation of different methods for handling missing covariate data showed that a correctly implemented multiple imputations method and full maximum likelihood modelling methods resulted in unbiased and precise parameter estimates when covariate data were MCAR or MAR. When the covariate data were MNAR, the only method resulting in unbiased and precise parameter estimates was a full maximum likelihood modelling method where an extra parameter was estimated, correcting for the unknown missing data mechanism's dependence on the missing data.

This thesis presents new insight to the dynamics of missing data in nonlinear mixed effects modelling. Strategies for handling different types of missing data have been developed and compared in order to provide guidance for efficient handling and consequences of inappropriate handling of missing data.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2014. 75 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 189
Keyword
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
National Category
Pharmaceutical Sciences
Research subject
Pharmaceutical Science
Identifiers
urn:nbn:se:uu:diva-224098 (URN)978-91-554-8970-0 (ISBN)
Public defence
2014-08-29, B41, BMC, Husargatan 3, Uppsala, 09:15 (English)
Opponent
Supervisors
Available from: 2014-05-27 Created: 2014-05-03 Last updated: 2014-06-30Bibliographically approved

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