Development, Application and Evaluation of Statistical Tools in Pharmacometric Data Analysis

Pharmacometrics uses models based on pharmacology, physiology and disease for quantitative analysis of interactions between drugs and patients. The availability of software implementing modern statistical methods is important for efficient model building and evaluation throughout pharmacometric data analyses. The aim of this thesis was to facilitate the practical use of available and new statistical methods in the area of pharmacometric data analysis. This involved the development of suitable software tools that allows for efficient use of these methods, characterisation of basic properties and demonstration of their usefulness when applied to real world data. The thesis describes the implementation of a set of statistical methods (the bootstrap, jackknife, case-deletion diagnostics, log-likelihood profiling and stepwise covariate model building), made available as tools through the software Perl-speaks-NONMEM (PsN). The appropriateness of the methods and the consistency of the software tools were evaluated using a large selection of clinical and nonclinical data. Criteria based on clinical relevance were found to be useful components in automated stepwise covariate model building. Their ability to restrict the number of included parameter-covariate relationships while maintaining the predictive performance of the model was demonstrated using the antiarrythmic drug dofetilide. Log-likelihood profiling was shown to be equivalent to the bootstrap for calculating confidence intervals for fixed-effects parameters if an appropriate estimation method is used. The condition number of the covariance matrix for the parameter estimates was shown to be a good indicator of how well resampling methods behave when applied to pharmacometric data analyses using NONMEM. The software developed in this thesis equips modellers with an enhanced set of tools for efficient pharmacometric data analysis