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The back-step method: method for obtaining unbiased population parameter estimates for ordered categorical data
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Division of Pharmacokinetics and Drug Therapy. (Farmakometri)ORCID iD: 0000-0003-3531-9452
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Division of Pharmacokinetics and Drug Therapy. (Farmakometri)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Division of Pharmacokinetics and Drug Therapy. (Farmakometri)
2004 (English)In: AAPS Journal, ISSN 1550-7416, Vol. 6, no 3, 13-22 p.Article in journal (Refereed) Published
Abstract [en]

A significant bias in parameters, estimated with the proportional odds model using the software NONMEM, has been reported. Typically, this bias occurs with ordered categorical data, when most of the observations are found at one extreme of the possible outcomes. The aim of this study was to assess, through simulations, the performance of the Back-Step Method (BSM), a novel approach for obtaining unbiased estimates when the standard approach provides biased estimates. BSM is an iterative method involving sequential simulation-estimation steps. BSM was compared with the standard approach in the analysis of a 4-category ordered variable using the Laplacian method in NONMEM. The bias in parameter estimates and the accuracy of model predictions were determined for the 2 methods on 3 conditions: (1) a nonskewed distribution of the response with low interindividual variability (IIV), (2) a skewed distribution with low IIV, and (3) a skewed distribution with high IIV. An increase in bias with increasing skewness and IIV was shown in parameters estimated using the standard approach in NONMEM. BSM performed without appreciable bias in the estimates under the 3 conditions, and the model predictions were in good agreement with the original data. Each BSM estimation represents a random sample of the population; hence, repeating the BSM estimation reduces the imprecision of the parameter estimates. The BSM is an accurate estimation method when the standard modeling approach in NONMEM gives biased estimates.

Place, publisher, year, edition, pages
2004. Vol. 6, no 3, 13-22 p.
Keyword [en]
ordered categorical, proportional odds model, bias in parameter estimates, NONMEM, Laplacian, pharmacodynamics
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:uu:diva-97669DOI: 10.1208/aapsj060319PubMedID: 15760104OAI: oai:DiVA.org:uu-97669DiVA: diva2:172698
Available from: 2008-10-30 Created: 2008-10-30 Last updated: 2015-01-23
In thesis
1. Methodological Studies on Models and Methods for Mixed-Effects Categorical Data Analysis
Open this publication in new window or tab >>Methodological Studies on Models and Methods for Mixed-Effects Categorical Data Analysis
2008 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Effects of drugs are in clinical trials often measured on categorical scales. These measurements are increasingly being analyzed using mixed-effects logistic regression. However, the experience with such analyzes is limited and only a few models are used.

The aim of this thesis was to investigate the performance and improve the use of models and methods for mixed-effects categorical data analysis. The Laplacian method was shown to produce biased parameter estimates if (i) the data variability is large or (ii) the distribution of the responses is skewed. Two solutions are suggested; the Gaussian quadrature method and the back-step method. Two assumptions made with the proportional odds model have also been investigated. The assumption with proportional odds for all categories was shown to be unsuitable for analysis of data arising from a ranking scale of effects with several underlying causes. An alternative model, the differential odds model, was developed and shown to be an improvement, in regard to statistical significance as well as predictive performance, over the proportional odds model for such data. The appropriateness of the likelihood ratio test was investigated for an analysis where dependence between observations is ignored, i.e. performing the analysis using the proportional odds model. The type I error was found to be affected; thus assessing the actual critical value is prudent in order to verify the statistical significance level. An alternative approach is to use a Markov model, in which dependence between observations is incorporated. In the case of polychotomous data such model may involve considerable complexity and thus, a strategy for the reduction of the time-consuming model building with the Markov model and sleep data is presented.

This thesis will hopefully contribute to a more confident use of models for categorical data analysis within the area of pharmacokinetic and pharmacodynamic modelling in the future.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2008. 76 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 83
Keyword
Pharmacodynamics, Categorical data, Markov model, Modelling, NONMEM, NLMIXED, Laplace, Gaussian quadrature, Back-Step Method, Proportional odds model, Differential odds model
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-9333 (URN)978-91-554-7316-7 (ISBN)
Public defence
2008-11-21, B42, BMC, Husargatan 3, Uppsala, 13:15
Opponent
Supervisors
Available from: 2008-10-30 Created: 2008-10-30 Last updated: 2012-06-01Bibliographically approved

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Kjellsson, Maria C.Karlsson, Mats O.

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