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Performance of Nonlinear Mixed Effects Models in the Presence of Informative Dropout
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (farmakometri)
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. (farmakometri)
2015 (English)In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 17, no 1, 245-255 p.Article in journal (Refereed) Published
Abstract [en]

Informative dropout can lead to bias in statistical analyses if not handled appropriately. The objective of this simulation study was to investigate the performance of nonlinear mixed effects models with regard to bias and precision, with and without handling informative dropout. An efficacy variable and dropout depending on that efficacy variable were simulated and model parameters were reestimated, with or without including a dropout model. The Laplace and FOCE-I estimation methods in NONMEM 7, and the stochastic simulations and estimations (SSE) functionality in PsN, were used in the analysis. For the base scenario, bias was low, less than 5% for all fixed effects parameters, when a dropout model was used in the estimations. When a dropout model was not included, bias increased up to 8% for the Laplace method and up to 21% if the FOCE-I estimation method was applied. The bias increased with decreasing number of observations per subject, increasing placebo effect and increasing dropout rate, but was relatively unaffected by the number of subjects in the study. This study illustrates that ignoring informative dropout can lead to biased parameters in nonlinear mixed effects modeling, but even in cases with few observations or high dropout rate, the bias is relatively low and only translates into small effects on predictions of the underlying effect variable. A dropout model is, however, crucial in the presence of informative dropout in order to make realistic simulations of trial outcomes.

Place, publisher, year, edition, pages
2015. Vol. 17, no 1, 245-255 p.
National Category
Pharmaceutical Sciences
Identifiers
URN: urn:nbn:se:uu:diva-202045DOI: 10.1208/s12248-014-9700-xISI: 000347448900023PubMedID: 25421458OAI: oai:DiVA.org:uu-202045DiVA: diva2:630676
Available from: 2013-06-19 Created: 2013-06-19 Last updated: 2017-12-06
In thesis
1. Pharmacometric Models in Anesthesia and Analgesia
Open this publication in new window or tab >>Pharmacometric Models in Anesthesia and Analgesia
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Modeling is a valuable tool in drug development, to support decision making, improving study design, and aid in regulatory approval and labeling. This thesis describes the development of pharmacometric models for drugs used in anesthesia and analgesia.

Models describing the effects on anesthetic depth, measured by the bispectral index (BIS), for a commonly used anesthetic, propofol, and for a novel anesthetic, AZD3043, were developed. The propofol model consisted of two effect-site compartments, and could describe the effects of propofol when the rate of infusion is changed during treatment. AZD3043 had a high clearance and a low volume of distribution, leading to a short half-life. The distribution to the effect site was fast, and together with the short plasma half-life leading to a fast onset and offset of effects. It was also shown that BIS after AZD3043 treatment is related to the probability of unconsciousness similar to propofol.

In analgesia studies dropout due to lack of efficacy is common. This dropout is not at random and needs to be taken into consideration in order to avoid bias. A model was developed describing the PK, pain intensity and dropout hazard for placebo, naproxen and a novel analgesic compound, naproxcinod, after removal of a wisdom tooth. The model provides an opportunity to describe the effects of other doses or formulations. Visual predictive checks created by simultaneous simulations of PI and dropout provided a good way of assessing the goodness of fit when there is informative dropout.

The performance of non-linear mixed effects models in the presence of informative dropout, with and without including models that describe such informative dropout was investigated by simulations and re-estimations. When a dropout model was not included there was in general more bias. The bias increased with decreasing number of observations per subject, increasing placebo effect and increasing dropout rate. Bias was relatively unaffected by the number of subjects in the study. The bias had, in general, little effect on simulations of the underlying efficacy score, but a dropout model would still be needed in order to make realistic simulations.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2013. 56 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 173
Keyword
Pharmacometrics, Anesthesia, Analgesia, Dropout, NONMEM
National Category
Pharmaceutical Sciences
Research subject
Pharmacokinetics and Drug Therapy
Identifiers
urn:nbn:se:uu:diva-205580 (URN)978-91-554-8726-3 (ISBN)
Public defence
2013-10-04, B22, Uppsala Biomedicinska Centrum (BMC), Husargatan 3, Uppsala, 13:15 (English)
Opponent
Supervisors
Available from: 2013-09-13 Created: 2013-08-20 Last updated: 2014-01-22

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Björnsson, MarcusFriberg, LenaSimonsson, Ulrika

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