title: A note on statistical analysis of organ weights in non-clinical toxicological studies.
abstract: Statistical comparison of organ weights between treated and untreated animals have traditionally been used to predict potential toxicity for patients. The manner of presentation of organ weight data, and the value of statistical analyses have been topics of discussion. Historically, a decision tree approach has been applied for statistical comparison of organ weights which does not control the overall error rate and can lead to different statistical tests being used by chance for identical settings causing confusion. This paper proposes a simple nonparametric approach for assessing treatment effects on organ weights in terms of ratios based on the Hodges-Lehmann estimator. This allows for simple interpretation of results and aids in the identification of potential target organs as the evaluation is based on effect sizes and not on p-values allowing a robust proof of effect as well as a robust proof of no effect. The proposed estimate and the corresponding nonparametric confidence interval applied to a rank-sum score can be used as a confirmatory test for difference and as a confirmatory test for equivalence. Exploratory analyses can be performed calculating the proposed estimates for each organ separately to be summarized graphically in a confidence interval plot.
authors.affiliation: Baxter Innovations GmbH, Wagramer Strasse 17-19, 1220 Vienna, Austria. martin_wolfsegger@baxter.com
authors.forename: Martin J
authors.initials: MJ
authors.lastname: Wolfsegger
authors.forename: Thomas
authors.initials: T
authors.lastname: Jaki
authors.forename: Barbara
authors.initials: B
authors.lastname: Dietrich
authors.forename: Jackie A
authors.initials: JA
authors.lastname: Kunzler
authors.forename: Kerry
authors.initials: K
authors.lastname: Barker
mesh_terms: D000818:Animals; D016001:Confidence Intervals; D006801:Humans; D009929:Organ Size / Q000187:drug effects / Q000502:physiology; D015203:Reproducibility of Results; D013223:Statistics as Topic; D018709:Statistics, Nonparametric; D014116:Toxicology / Q000379:methods* / Q000706:statistics & numerical data*