Model selection in toxicity studies

Wei Liu*, Jian Tao, Ning Zhong Shi, Man Lai TANG

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

1 Citation (Scopus)

Abstract

In toxicity studies, model mis-specification could lead to serious bias or faulty conclusions. As a prelude to subsequent statistical inference, model selection plays a key role in toxicological studies. It is well known that the Bayes factor and the cross-validation method are useful tools for model selection. However, exact computation of the Bayes factor is usually difficult and sometimes impossible and this may hinder its application. In this paper, we recommend to utilize the simple Schwarz criterion to approximate the Bayes factor for the sake of computational simplicity. To illustrate the importance of model selection in toxicity studies, we consider two real data sets. The first data set comes from a study of dietary fortification with carbonyl iron in which the Bayes factor and the cross-validation are used to determine the number of sub-populations in a mixture normal model. The second example involves a developmental toxicity study in which the selection of dose-response functions in a beta-binomial model is explored.

Original languageEnglish
Pages (from-to)418-431
Number of pages14
JournalStatistica Neerlandica
Volume63
Issue number4
DOIs
Publication statusPublished - Nov 2009

Scopus Subject Areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

User-Defined Keywords

  • Bayes factor
  • Beta-binomial model
  • Cross validation
  • Finite mixture model

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