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- Kjellsson, Maria C., 1975-
(författare)
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Methodological Studies on Models and Methods for Mixed-Effects Categorical Data Analysis
- 2008
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 2. |
- Jönsson, Siv, 1963-
(författare)
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Estimation of Dosing Strategies for Individualisation
- 2004
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- To increase the proportion of patients with successful drug treatment, dose individualisation on the basis of one or several patient characteristics, a priori individualisation, and/or on the basis of feedback observations from the patient following an initial dose, a posteriori individualisation, is an option. Efficient tools in optimising individualised dosing strategies are population models describing pharmacokinetics (PK) and the relation between pharmacokinetics and pharmacodynamics (PK/PD).Methods for estimating optimal dosing strategies, with a discrete number of doses, for dose individualisation a priori and a posteriori were developed and explored using simulated data. The methods required definitions of (i) the therapeutic target, i.e. the value of the target variable and a risk function quantifying the seriousness of deviation from the target, (ii) a population PK/PD model relating dose input to the target variable in the patients to be treated, and (iii) distributions of relevant patient factors. Optimal dosing strategies, in terms of dose sizes and individualisation conditions, were estimated by minimising the overall risk. Factors influencing the optimal dosing strategies were identified. Consideration of those will have implications for study design, data collection, population model development and target definition.A dosing strategy for a priori individualisation was estimated for NXY-059, a drug under development. Applying the estimated dosing strategy in a clinical study resulted in reasonable agreement between observed and expected outcome, supporting the developed methodology.Estimation of a dosing strategy for a posteriori individualisation for oxybutynin, a drug marketed for the treatment of overactive bladder, illustrated the implementation of the method when defining the therapeutic target in terms of utility and responder probability, that is, as a combination of the desired and adverse effects.The proposed approach provides an estimate of the maximal benefit expected from individualisation and, if individualisation is considered clinically superior, the optimal conditions for individualisation. The main application for the methods is in drug development where the methods can be generally employed in the establishment of dosing strategies for individualisation with relevant extensions regarding population model complexity and individualisation conditions.
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