Tree-type algorithm for statistical analysis in chronic toxicity studies

Abstract

An appropriate statistical methodology in toxicity studies has been discussed over the last two decades and many statistical methods have already been proposed. Many practical problems, however, still remain unresolved and most pharmaceutical industries have been using a tree-type algorithm routinely to analyze repeated-dose toxicity study data. In considering routine use of statistical analysis in toxicological studies, standardization of statistical methodology is necessary and the decision tree has an important role. In this article, the problems, relating to tree-type algorithms are summarized. Then we propose a new tree-type algorithm, which targets quantitative data in repeated-dose studies in rodents, usually sample size per group between 10 to 20, based on the following two important principles: 'using a parametric method' and 'suitable for intuition of toxicologists'. An example of its application to actual toxicity study data is demonstrated. The performance of this new method is also evaluated using historical data. However, it should be noted that the intention of this paper is not to make a definite solution of the decision tree. Several other alternatives can be considered. Since there is no single theoretically correct solution of tree-type algorithms, too formal a use of the decision tree is not recommended. We must not forget the exploratory nature of evaluating repeated toxicity data.