An experimental design for judging synergism on consideration to endocrine disruptor animal experiments

This paper investigates an appropriate statistical design for an animal experiment to evaluate synergism of two test chemicals. It assumes a certain number of animals are divided into groups, each of which is treated with a combination of dose levels of two chemicals. A design is identified by the set of group size for each combination of doses, including the case where the dose of either one chemical is zero. The power of t‐test to detect synergism by positive surplus of response on a simultaneous administration group from the additivity plane composed of the responses on single administration groups is adopted as the criterion for the appropriate design. The applicable design is investigated for the application to real cases of endocrine disrupter study conducted at the National Institute of Health Sciences of Japan.

[1]  Luc Bijnens,et al.  Design and Analysis of Drug Combination Experiments , 2005, Biometrical journal. Biometrische Zeitschrift.

[2]  Hong-Bin Fang,et al.  Experimental design and sample size determination for testing synergism in drug combination studies based on uniform measures , 2003, Statistics in medicine.

[3]  J. Kanno,et al.  A statistical method for judging synergism: application to an endocrine disruptor animal experiment , 2003 .

[4]  J Ashby,et al.  The OECD program to validate the rat uterotrophic bioassay to screen compounds for in vivo estrogenic responses: phase 1. , 2001, Environmental health perspectives.

[5]  J. Estève,et al.  Using relative risk models for estimating synergy between two risk factors. , 1998, Statistics in medicine.

[6]  W. H. Carter,et al.  Erratum: Detection of Departures from Additivity in Mixtures of Many Chemicals with a Threshold Model , 1997 .

[7]  K. Imaida,et al.  Analysis of Synergism in Hepatocarcinogenesis Based on Preneoplastic Foci Induction by 10 Heterocyclic Amines in the Rat , 1996, Japanese journal of cancer research : Gann.

[8]  Chris Gennings,et al.  Utilizing Concentration-Response Data from Individual Components to Detect Statistically Significant Departures from Additivity in Chemical Mixtures , 1995 .

[9]  S G Machado,et al.  A direct, general approach based on isobolograms for assessing the joint action of drugs in pre-clinical experiments. , 1994, Statistics in medicine.

[10]  C. Kelly,et al.  Monotone smoothing with application to dose-response curves and the assessment of synergism. , 1990, Biometrics.

[11]  E. Laska,et al.  Testing whether an identified treatment is best. , 1989, Biometrics.

[12]  Berenbaum Mc What is synergy? , 1989, Pharmacological reviews.

[13]  R. L. Plackett,et al.  Experimental Design for Joint Action , 1982 .

[14]  R. Plackett,et al.  A Unified Theory for Quantal Responses to Mixtures of Drugs: Non-Interactive Action , 1959 .