title: Design of a testing strategy using non-animal based test methods: lessons learnt from the ACuteTox project.
abstract: In the framework of toxicology, a testing strategy can be viewed as a series of steps which are taken to come to a final prediction about a characteristic of a compound under study. The testing strategy is performed as a single-step procedure, usually called a test battery, using simultaneously all information collected on different endpoints, or as tiered approach in which a decision tree is followed. Design of a testing strategy involves statistical considerations, such as the development of a statistical prediction model. During the EU FP6 ACuteTox project, several prediction models were proposed on the basis of statistical classification algorithms which we illustrate here. The final choice of testing strategies was not based on statistical considerations alone. However, without thorough statistical evaluations a testing strategy cannot be identified. We present here a number of observations made from the statistical viewpoint which relate to the development of testing strategies. The points we make were derived from problems we had to deal with during the evaluation of this large research project. A central issue during the development of a prediction model is the danger of overfitting. Procedures are presented to deal with this challenge.
authors.affiliation: Department of Biostatistics, German Cancer Research Center, DKFZ, Im Neuenheimer Feld 280, D-69009 Heidelberg, Germany. kopp@dkfz.de
authors.forename: Annette
authors.initials: A
authors.lastname: Kopp-Schneider
authors.forename: Pilar
authors.initials: P
authors.lastname: Prieto
authors.forename: Agnieszka
authors.initials: A
authors.lastname: Kinsner-Ovaskainen
authors.forename: Sven
authors.initials: S
authors.lastname: Stanzel
mesh_terms: D000465:Algorithms; D000826:Animal Testing Alternatives; D000818:Animals; D003627:Data Interpretation, Statistical; D006801:Humans; D008954:Models, Biological; D023382:Toxicity Tests, Acute