Dose Finding Using Bayesian Decision Theory in Early Phase Clinical Pharmacology Trials
Scott D Patterson, Dawn Webber, John Whitehead
Abstract: In phase I clinical trials, experimental drugs are administered to healthy volunteers in order to establish their safety and to explore the relationship between the dose taken and the concentration found in plasma. Each volunteer receives a series of increasing single doses. In this paper a Bayesian decision procedure is developed for choosing the doses to give in the next round of the study, taking into account both prior information and the responses observed so far. The procedure seeks the optimal doses for learning about the dose-concentration relationship, subject to a constraint which reduces the risk of administering dangerously high doses.
Individual volunteers receive more than one dose, and the pharmacokinetic responses observed are, after logarithmic transformation, treated as approximately normally distributed. Thus data analysis can be achieved by fitting linear mixed models. By expressing prior information as “pseudo-data”, and by maximising over posterior distributions rather than taking expectations, a procedure which can be implemented using standard mixed model software is derived. Comparisons are made with existing approaches to the conduct of these studies, and the new method is illustrated using real and simulated data.
Direct Method of Selecting Informative Variables
Valerii Fedorov, David Gruben, and Sergei Leonov
Abstract: We discuss methods which select the most informative subset of variables from the variables which can be directly measured. It is shown that for problems of relatively small dimension, an exhaustive search algorithm based on simple recursive formulae works quite well while for larger dimensions algorithms based on ideas of optimal experimental design are more efficient. An example based on real clinical data is considered.
The Total Least Squares Method In Individual Bioequivalence Evaluation
Vladimir Dragalin and Valerii Fedorov
Abstract. We propose a simple method for comparison of series of matched observations. While in all our examples we address “individual bioequivalence”, which is the subject of much discussion in pharmaceutical statistics, the methodology can be applied to a wide class of cross-over experiments, including cross-over imaging. From the statistical point of view the considered models belong to the class of the “error-in-variables” models. In computational statistics the corresponding optimization method is referred to as the “least squares distance” and the “total least squares” method. Simple simulations and real-life examples show that the proposed method is very intuitive and transparent, and, at the same time, has a solid statistical and computational background.
Kullback-Leibler Distance for Evaluating Bioequivalence: I. Criteria and Models
Vladimir Dragalin and Valerii Fedorov
Abstract: In this paper we propose a methodology for evaluating bioequivalence of two formulations of a drug that encompasses the average bioavailability, prescribability and switchability aspects. The main idea is to use the Kullback-Leibler distance as a measure of discrepancy between the distributions of the two formulations.
Interface-guided Ontology Design
Gary H. Merrill
Abstract: An application-oriented methodology, focusing on the role of the user interface, is proposed to address a set of classical problems faced in the design and development of ontologies. The set of problems and their consequences are discussed in detail, and the value of an interface-guided approach to ontology design is demonstrated on the basis of this analysis and experience using such a methodology.
