03.mdi

2011 Triveni Enterprises J. Environ. Biol.
Vikas Nagar, Lucknow, INDIA Full paper available on: www.jeb.co.in Statistical tools for analysing the data obtained from repeated dose toxicity studies with rodents: A comparison of the statistical tools used in Japan with that of used in other countries Katsumi Kobayashi National Institute of Technology and Evaluation (NITE), 2-49-10 Nishihara, Shibuya-ku, (Corresponding author) Tokyo - 151 0066, Japane-mail: kobayashi@beige.plala.or.jp K. Sadasivan Pillai Frontier Lifeline, International Centre for Cardio-Thoracic and Vascular Diseases,Chennai - 600 101, India Frontier Lifeline, International Centre for Cardio-Thoracic and Vascular Diseases,Chennai - 600 101, India Frontier Lifeline, International Centre for Cardio-Thoracic and Vascular Diseases,Chennai - 600 101, India National Institute of Technology and Evaluation (NITE), 2-49-10 Nishihara, Shibuya-ku,Tokyo 151-0066, Japan In the present study, an attempt was made to compare the statistical tools used for analysing the data of repeateddose toxicity studies with rodents conducted in 45 countries, with that of Japan. The study revealed that therewas no congruence among the countries in the use of statistical tools for analysing the data obtained from the above studies. For example, to analyse the data obtained from repeated dose toxicity studies with rodents,Scheffé's multiple range and Dunnett type (joint type Dunnett) tests are commonly used in Japan, but in other countries use of these statistical tools is not so common. However, statistical techniques used for testing the above data for homogeneity of variance and inter-group comparisons do not differ much between Japan and othercountries. In Japan, the data are generally not tested for normality and the same is true with the most of the Revised received: countries investigated. In the present investigation, out of 127 studies examined, data of only 6 studies were analysed for both homogeneity of variance and normal distribution. For examining homogeneity of variance, wepropose Levene's test, since the commonly used Bartlett's test may show heterogeneity in variance in all the groups, if a slight heterogeneity in variance is seen any one of the groups. We suggest the data may be examined for both homogeneity of variance and normal distribution. For the data of the groups that do not showheterogeneity of variance, to find the significant difference among the groups, we recommend Dunnett's test, andfor those show heterogeneity of variance, we recommend Steel's test.
Statistical tools, Toxicity study, Rodents, Cluster analysis, Dunnett's test, Steel's test studies with rodents, none of them gives a clear picture on the It is a regulatory requirement in most of the countries that statistical tools to be used for analysing the data obtained from these toxicity studies are conducted in animal models as per appropriate studies. However, it is mentioned in these guidelines that the statistical guidelines for registering industrial chemicals with the respective methods should be selected during the design of the study. Selection Government agencies of the countries. Though most of the regulatory of a non-appropriate statistical tool during the design of the study or guidelines, for example, OECD (1995), EPA (2000) and FDA (2003) using a different statistical tool from that mentioned in the study plan give sufficient information on the conduct of repeated dose toxicity with improper justification at the end of the study may lead to Journal of Environmental Biology January 2011 Kobayashi et al.
Table - 1: Statistical methods used in various countries to analyse the data obtained from repeated dose toxicity studies with rodents [1]Statistical method, [2]Test substance, [3]Reference [1] Student t-test, [2] Ammonium nitrate, [3] African J. Biotech., 5, 749-754, 2006.
[1] (t) student test and Dunnett method, [2] Diflubenzuron, [3] Sci. Res. Essay, 2, 79-83, 2007.
[1] Kruskall-Wallis test, ANOVA, [2] 1, 2-dimethylhydrazine, [3] Biocell (Mendoza), 26, 3, Mendoza ago./dez., 2002.
[1] ANOVA, Student-Newman-Keuls test, [2] Chitosan, [3] J. Leukocyte Biol., 78, July 2005.
[1] ANOVA, Tukey honest test, Kolmogorov-Smirnov test [2] Bupivacaine and ropivacaine, [3] Anesth. Analg., 91, 1489-1492, 2000.
[1] ANOVA, [2] Chitosan-DNA nanoparticles, [3] AAPS Pharm. Sci. Tech., 5, 2004.
[1] ANOVA, [2] Methylmethacrylate, [3] Braz. Oral Res., 19 Sao Paulo, July/Sept., 2005.
[1] ANOVA followed by the Tukey multiple comparison test, [2] Cordia salicifolia extract, [3] Acta Sci. Health Sci., 27, 4144, 2005.
[1] ANOVA fol owed by the Student-Newman Keuls test, [2] Hydro-ethanolic extract of leaves of Senna alata (L.), [3] African J. Biotech.,5, 283-289, 2006.
[1] ANOVA and Duncan's multiple range tests, [2] Hibiscus cannabinus, [3] African J. Biotech., 4, 833-837, 2005.
[1] One-way ANOVA followed by Newman–Keuls test [2] Amylin Receptor Blocks-Amyloid, [3] J. Neurosci., 24, 5579-5584, 2004.
[1] Student's t-test, ANOVA, Dunnett's multiple comparison test, [2] Hexachlorobenzene, [3] Environ. Health Perspectives, 111, 4, 2003.
[1] F-ANOVA and a post-hoc test (Fisher's protected partial least square test), [2] Substantia Nigra and Neostriatum, [3] J. Neurochem.,83, 645–654, 2002.
[1] ANOVA followed by a post hoc Newman–Keuls' multiple comparison test, Student's t test, [2] Manganese, [3] Pharm. Biochem.
Behavior, 77, 245–251, 2004.
Chile, Costa Rica [1] ANOVA followed by Dunnett's multiple comparison test, [2] Aloysia polystachya, [3] http://captura.uchile.cl/dspace/ [1] One-way ANOVA, [2] Monosialoganglioside, [3] Acta Pharmacol. Sin, 25, 727-732, 2004.
[1] Student's t-test, [2] Pyrethroid, [3] J. Occup. Health, 38, 54-56, 1996.
[1] One-way ANOVA, Kruskal-Wallis test, Mann-Whitney U test, [2] Hyperbaric oxygen preconditioning, [3] Chinese Med. J., 113,837-839, 2000.
[1] One-way ANOVA, [2] GM1 ganglioside, [3] J. Zhejiang Univ. Sci. B., 6 (4), 254-258, 2005.
[1] Normality assumptions (Kolmogorov-Smirnov's and Shapiro-Wilk's tests), Levene's test, ANOVA, Kruskall-Wallis's test, t pairedtests or Wilcoxon's test, [2] Granulocyte-Colony Stimulating Factor (G-CSF), [3] Biotecnología Aplicada, 22, 50-53, 2005.
[1] Mann-Whitney test, [2] D-002, [3] Biotecnología Aplicada, 18, 88-90, 2001.
[1] ANOVA, Students–Newman–Keuls post hoc test, [2] Kainate, [3] Eur. J. Pharmacol., 390, 295-298, 2000.
[1] t-test after F-test, Normal distribution, [2] Aflatoxin B1 and T-2 toxin, [3] Vet. Med. Czech, 46, 301-307, 2001.
[1] One-way ANOVA test, Tukey-Kramer's post hoc test, [2] D-galactosamine, [3] Physiol. Res., 55, 551-560, 2006.
[1] Mann-Whitney U test and ANOVA, [2] Novispirin G10, [3] Antimicrobial Agent and Chemotherapy, 49, 3868-3874, 2005.
[1] One-way ANOVA, Student's paired t test, [2] Bendroflumethiazide, [3] J. Pharmacol. Exp. Ther., 299, 307-313, 2001.
[1] Shapiro Wilks test, Levene's test, General linear model (GLM) analysis, [2] Diesel exhaust particles, [3] Carcinogenesis. 24. 1847-1852, 2003.
[1] One way ANOVA fol owed by Tukey–Kramer test for multiple comparison, [2] Benzo(a)pyrene, Nigella sativa seeds, [3] Food Chem.
Toxicol., 45, 88-92, 2007.
[1] Student's t test, [2] Garlic extract, [3] Res. J. Med. Medical Sci., 1, 85-89, 2006.
[1] ANOVA followed by Student Newman-Keuls test, [2] Oxygen, [3] Eur. Respir. J., 9, 2531-2536, 1996.
[1] Mann-Whitney nonparametric U test, Kruskall-Wallis analyses with a Dunn's post test, [2] Doxorubicin, [3] Cancer Res., 61, 6423–6427, 2001.
[1] ANOVA, Student t test, [2] Amphotericin B, [3] Antimicrob. Agents Chemother., 35, 1303–1308, 1991.
[1] Bartlett's test, ANOVA, Dunnett's test, Kruskal-Wallis test, Dunn's test, [2] 5á-reductase (5áR) inhibitor, [3] Molecular & CellularProteomics, July 12, 2006.
[1] ANOVA, Dunnett's test, [2] Ethylene oxide, [3] Fund. Appl. Toxicol., 34, 223-227, 1996.
[1] ANOVA, Ryan-Einot-Gabriel-Walsh test, Nonparametric test (van der Waerden test using normalized scores), [2] Polybrominateddiphenyl ethers, [3] Environ. Health Perspectives, 114, 2006 [1] Two-factor analysis of variance with a Bonferroni correction, [2] Polychlorinated biphenyls (PCBs), [3] Environ. Health Perspectives,109, 2001.
[1] Two-way analysis of variance, LSD, [2] Urethan, [3] J. Appl. Physiol., 81, 2304-2311, 1996.
[1] One-way analysis of variance and an unpaired Student's t-test, [2] BPV, [3] Anesth. Analg., 85, 1337-1343, 1997.
[1] t test, [2] 3-nitropropionic acid, [3] Arh. Hig. Rada. Toksikol., 56, 297-302, 2005.
[1] One-way ANOVA with LSD post hoc test, after the Kolmogorov-Smirnov normality, [2] Heavy metals, organophosphates, [3] Arh.
Hig. Rada.Toksikol., 56, 257-264, 2005.
[1] ANOVA and Dunnett test, Student t test, [2] Polyoxyethylene glycol, [3] AAPS Pharm. Sci., 6, 2004.
[1] One-way ANOVA, Student's t-test, [2] Galactose, [3] Human Reproduction, 18, 2031-2038, 2003 [1] Bartlett's test, ANOVA and Student's t test, [2] Fluoride, [3] Fluoride, 30, Research Report 105, 1997.
[1] ANOVA and Dunnett test, Student t test, [2] Novel surfactants, [3] AAPS Pharm. Sci., 6, Article 14, 2004.
[1] ANOVA, Tukey's test, [2] PUFA concentrate, [3] African J. Biotech, 6, 1021-1027, 2007.
[1] ANOVA followed by multiple comparison test of Newman-Keuls test, [2] Fumaria parviflora Lam., [3] DARU, 12, 136-140, 2004.
Journal of Environmental Biology January 2011 Comparison of statistical tools used in Japan with other countries [1] Kruskal-Wallis test, [2] Valproic acid, [3] DARU, 14, No. 1, 2006 [1] Shaphiro-Wilk, ANOVA, Tukey-Kramer, [2] Nitrogen and helium, [3] J. Appl. Physiol., 98, 144-150, 2005.
[1] Student's t test, [2] AS 101, [3] J. National Cancer Inst., 88, 1996.
[1] Wilcoxon rank-sum test, [2] Juglans regia L., Olea europea L., Urtica dioica L. and Atriplex halimus L., [3] eCAM Advance AccessPublished online on May 17, 2007.
[1] Student t test, [2] HD-Ad Vector, [3] PNAS, 102, 3930-3935, 2005.
[2] ANOVA followed by Tukey test, [2] MDMA, [3] BMC Neuroscience, 7, 13, 2006.
[1] Bartlett's test, Dunnett's test, Scheffe's test, Kruskal-Wal is ranking analysis, Dunnett type (Hollander and Wolf), [2] 4-nitrophenol and2,4-dinitrophenol, [3] J. Toxicol. Sci., 26, 299-311, 2001.
[1] Student t- test, [2] 5-FU, [3] J. Toxicol. Sci., 27, 49-56, 2002.
[1] Bartlett's test, one-way layout analysis of variance, Kruskal-Wallis test, Mann-Whitney's U test, Dunnett type (Hollander and Wolf),[2] 3-Aminiphenol, [3] Toxicol. Sci., 27, 411-421, 2002.
[1] Bartlett's test, Dunnett's or Scheffé's tests, Kruskal-Wallis ranking test, Dunnett type, Scheffe type or Mann-Whitney's U tests, [2]3-methylphenol, [3] J. Toxicol. Sci., 28, 59-70, 2003.
[1] Dunnett's multiple comparison test, [2] 1-carboxy-5,7- dibromo-6-hydroxy-2,3,4- trichloroxanthone, [3] J. Toxicol. Sci., 28, 445-453,2003.
[1] Bartlett test, Dunnett's test, Dunnett type rank sum test, [2] Wormwood, [3] J. Toxicol. Sci., 28, 471-478, 2003.
[1] Dunnett's test, ANOVA, [2] Rice bran glycosphingolipid, [3] J. Toxicol. Sci., 29, 73-80, 2004.
[1] Dunnett's multiple comparison test, [2] Dunnett's multiple comparison test, [3] J. Toxicol. Sci., 29, 505-516, 2004.
[1] Bartlett's test, Dunnett's test, Steel's multiple comparison test, Dunnett type (Hol ander and Wolf) Student's t-test, Aspin-Welch's t test,[2] 1, 3-dibromopropane, 1, 1, 2, 2-Tetrabromoethane, [3] J. Toxicol. Sci., 30, 29-42, 2005.
[1] ANOVA, Dunnett's or Scheffe's multiple comparison procedures, [2] 2,2'-isobutylidenebis (4,6-dimethylphenol), [3] J. Toxicol. Sci.,30, 275-285, 2005.
[1] Dunnett' multiple comparison test, [2] 2,3,7,8-tetrabromodibenzo-p-dioxin, [3] J. Toxicol. Sci., 32, 47-56, 2007.
[1] Bartlett's test, Dunnett's test, Dunnett-type method, [2] Water-Miscible Coenzyme Q10, [3] J. Health Sci., 51, 346-356, 2005.
[1] Bartlett, ANOVA, Kruskal-Wallis, Dunnett, Scheffe, Dunnett type, Scheffe type, [2] 2,3,3,3,2',3',3',3'-Octachlorodipropyl ether (S-421), [3] Bull. Natl. Inst. Health Sci., 121, 40-47, 2003.
[1] Dunnett's test or a Dunnett-type rank-sum test, [2] Bismuth, [3] J. Occup. Health, 47, 293-298, 2005.
[1] Bartlett's test, one-way ANOVA, Dunnett Kruskal-Wallis rank sum test, Dunnett type, [2] p-dichlorobenzene (pDCB), [3] J. Occup.
Health, 47, 249-260, 2005.
[1] ANOVA, Wilcoxon-rank sum test, [2] Ferula harmonis ‘zallouh', [3] Int. J. Impotence Res., 13, 247-251, 2001.
[1] t test, [2] Plant Extract NASFIT, [3] Int. J. Impotence Res., 13, 247-251, 2001.
[1] Levene's test, Dunnett's t-test, ANOVA, t-test, [2] Organic germanium fortified yeasts, [3] J. Toxicol. Sci., 29, 541-553, 2004.
[1] ANOVA followed by a modified Z-test (LSD), [2] Nitric oxide, [3] Invest. Ophthalmol. Visual Sci., 38, 995-1002, 1997.
[1] ANOVA by Bonferroni's post hoc comparison, [2] Pinellia ternata extract, [3] Biol. Pharm. Bull., 29, 1278-1281, 2006.
[1] General Linear Model, one-way ANOVA, Scheffe test, Kruskal Wallis test, Mann-Whitney test, [2] Aqueous extract of Labisia pumilavar. alata (LPA) or ‘Kacip Fatimah', [3] Indian J. Pharmacol., 39, 30-32, 2007.
[1] Student's t-test, [2] Yohimbine, [3] Biokemistri, 15, 50-56, 2003.
[1] Student's t, Mann-Whitney, [2] Follicle-Stimulating hormone, [3] Environ. Health Perspectives, 113, 2005.
[1] ANOVA fol owed by Fisher's least significant difference (LSD), [2] Perfluorooctane sulfonate, [3] Environ. Health Perspectives, 2003.
[1] ANOVA, [2] Ethanol, [3] Pharmacol., 282, 1028-1036, 1997.
[1] Dunnett's tests, [2] Butyl benzyl phthalate, [3] Toxicol. Sci., 94, 282-292, 2006.
[1] Student's t test, [2] N-Acetylcysteine Protects, [3] Am. J. Respir. Crit. Care Med., 157, 1283-1293, 1998.
[1] Student's t-test, [2] Ethanol extract of the leaves of Datura stramonium, [3] African J. Biotech., 6, 1012-1015, 2007.
[1] Student's t-test, [2] Trypanosoma brucei-infected, [3] African J. Biotech., 5, 1557-1561, 2006.
[1] ANOVA, Duncan's multiple range tests, [2] Lemongrass and green tea, [3] African J. Biotech., 5, 1227-1232, 2006.
[1] ANOVA, Duncan's multiple range tests, [2] Crude oil, [3] African J. Biotech., 3, 346-348, 2004.
[1] Student's t-test, [2] Salicylic acid and anthranilic acid, [3] African J. Biotech., 3, 426-431, 2004.
[1] ANOVA and Dunnett's multiple test, [2] GM3 cancer vaccine, [3] Pakistan J. Biol. Sci., 87, 1045-1050, 2005.
[1] Student's t-test, [2] Tanacetum, [3] Arch. Pharm. Res., 30, 303-312, 2007.
[1] Student t-test, ANOVA, [2] Ethylenebisdithiocarbamates, [3] Environ. Health Perspectives, 112, 2004.
[1] Student's one-tailed t-test, [2] Excitatory amino acid-induced toxicity, [3] Acta Neurobiol. Exp., 60, 365-369, 2000.
[1] ANOVA followed by Student's t-test, Newman-Keuls test, Mann-Whitney ranking test, [2] (S)-3,5-DHPG, [3] Pol. J. Pharmacol., 54,11-18, 2002.
[1] Student's t-test, [2] Prednisolone, [3] Pharm. Report, 59, 59-63, 2007.
[1] ANOVA, Newman-Keules test, [2] Endothelin-1, [3] Pharma. Report, 59, 98-105, 2007.
[1] ANOVA followed by Duncan's test or Student's t-test, [2] Caffeine, [3] Pharm. Report, 59, 296-305, 2007.
[1] One-way ANOVA followed by post hoc Duncan's test, Kruskal-Wallis ANOVA, Mann-Whitney U, [2] Methotrexate, [3] Pharm.
Report, 59, 359-364, 2007.
[1] Paired t test, two-way ANOVA, Tukey's, [2] ETB receptor, [3] Society for Experimental Biology and Medicine, Department ofPhysiology, Faculty of Medicine, University of Porto, Portugal, 2006.
Journal of Environmental Biology January 2011 Kobayashi et al.
[1] Two-Way ANOVA, followed by Bonferroni pos hoc test, [2] Amphetamine sulfate and HA bromide, [3] J. Health Sci., 53, 371-377,2007.
Republic of Slovenia[1] Scheffe's-test, ANOVA, [2] 2,4-dichlorophenoxyacetic acid, [3] Acta Vet. BRNO, 68, 281-229, 1999.
Russia [1] Student's –test, [2] Olypiphate, [3] Exp. Oncol., 25, 256-259, 2003.
[1] One-way ANOVA followed by Dunnett's test, [2] Methamphetamine, [3] JPET, 288, 1298-1310, 1999.
[1] Snedecor and Cochran, [2] Euphorbia heliscopia, [3] Pakistan J. Nutr., 5, 135-140, 2006.
[1] One-way ANOVA followed by Dunnet's or Tukey's for multiple comparison tests, [2] HgCl & DMPS, [3] Med. Sci. Monit., 12, 95- [1] Unpaired Student's t-test and the Mann-Whitney U-test, [2] Pyridoindole antioxidant stobadine, [3] Mol. Vision, 11, 56-65, 2005.
[1] Student's t-test, linear regression analysis with Pearson´s correlation coefficient, [2] Rooibos Tea (Aspalathus linearis), [3] Physiol.
Res., 53, 515-521, 2004.
[1] Student's t test, [2] Liver preservation solution, [3] Transplantation, 70, 430-436, 2000.
[1] ANOVA, [2] Fumonisin B1, [3] Carcinogenesis, 25, 1257-1264, 2004.
[1] Student's t-test, [2] Ethanol and/ or chloroquine fed normal or low protein diet, [3] Internet J. Hematol., ISSN: 1540-2649, 1996 to 2008.
http://www.ispub.com/ostia/index.php?xmlFilePath=journals/ijhe/vol3n1/chloroquine.xml [1] Shapiro-Wilks test, Levene test, ANOVA, followed by Tukey, Kruskal-Wallis test, Mann-Whitney U test, [2] Ochratoxin A, [3] FoodChem. Tox., 42, 825-834, 2004.
[1] ANOVA , [2] Pomegranate ellagitannin, [3] J. Agric. Food Chem., 51, 3493-3501, 2003.
[1] 2-way ANOVA followed by the Bonferroni method for multiple comparisons, Kruskal-Wallis test and the Mann-Whitney U-test, [2]Uranyl acetate dehydrate, [3] Exp. Biol. Med., 228, 1072-1077, 2003.
[1] Student's t-test, [2] Monoamine, [3] Neuropsychopharmacology, 25, 204-212, 2001.
[1] ANOVA, Kruskal-Wallis test, Mann-Whitney U test and Dunnett, [2] PentaBDE, [3] Organohalogen compounds, 68, 2006.
NetherlandsSweden and [1] Two-way ANOVA, Student t-test, [2] Hexabromocyclododecane, [3] Toxicol. Sci., 94, 281-292, 2006.
[1] Student's t-test, [2] Artemether, [3] Am. J. Trop. Med. Hyg., 66, 30-34, 2002.
[1] ANOVA and Dunnett´s post hoc test, [2] Amiodarone and amiodarone derivatives, [3] JPET Fast Forward. Published in September13, 2006 [1] ANOVA, Duncan multiple range test, [2] Guttiferae, [3] http://www.grad.chula.ac.th/gradresearch6/pdf/96.pdf [1] One-way ANOVA, Dunnett multiple ranges test, [2] Hyptis suaveolens, [3] Songklanakarin J. Sci. Technol., 27, 2005.
[1] ANOVA, Duncan multiple range test, [2] Methomyl [3] Arch. Hig. Rada. Toksikol., 49, 231-238, 1998.
[1] ANOVA, [2] Topical Formulation of Hyptis suaveolens oil, [3] CMU J., 5, 369-379, 2006.
[1] Kruskal-Wallis, Dunn's multiple comparison tests, [2] Ofloxacin, [3] Turk . J. Med. Sci. 30, 441-447, 2000.
[1] ANOVA, [2] Vitamin C, [3] Gen. Physiol. Biophys., 24, 47-55, 2005.
[1] ANOVA, Tukey and Dunnett tests, [2] Momordica charantia L. (Bitter melon) fruit extract, [3] African J. Biotech., 6, 273-277, 2007.
[1] Student's t-test, [2] 3-(4-methylbenzylidine) camphor, [3] Environ. Health Perspectives., 110, 2002.
[1] ANOVA and analysis of covariance (ANCOVA), [2] Mixtures of estrogens, [3] Environ. Health Perspectives, 112, 2004.
[1] ANOVA, Bartlett's test, Dunnett test, [2] TiO Rods and Dots, [3] Toxicol. Sci., 91, 227-236, 2006.
[1] ANOVA, Bartlett's test, Dunnett test, [2] TiO particles, [3] Particle Fibre Toxicol., 3, 2006.
[1] Fisher's least significant difference, [2] Folate status, [3] Blood, 92, 2471-2476, 1988.
[1] ANOVA followed by Dunnett's method, [2] Nanoparticles, [3] Toxicol. in vitro, 19, 975-983, 2005.
[1] ANOVA, Dunnett's , Bartlett's test, Kruskal–Wallis or Dunn's test, Levene's test, Shapiro–Wilk test, [2] Kidney bean (Phaseolusvulgaris) extract, [3] Food Chem. Toxicol., 45, 32-40, 2007.
Table - 2: Grouping the studies in clusters Statistical tools used The parametric data were analyzed by Dunnett' test and the nonparametric data were by Dunnett type rank sum test or Dunn's multiplecomparison test.
The parametric data were analyzed by Dunnett' or Scheffe' tests. The nonparametric data were analyzed by Dunnett type rank sum test.
After carrying out ANOVA or the data were directly subjected to Dunnett's, Duncan's, Student's or Mann-Whitney tests.
The homogeneity was examined by Levene's test, which has of low detection power. Data were also examined for normality Table - 3: Number of studies subjected to homogeneity and/or normality tests misinterpretation of the data. This may have tremendous negativeimpact in assessing the safety of the chemical, as the regulatory Test for homogeneity or normality bodies heavily rely on these data for assessing the safety.
Levene's homogeneity test+Shapiro-Wilks Levene's homogeneity test We made an attempt to compare the statistical tools used in Shapiro-Wilks test 45 countries for analysing the quantitative data obtained from 127 repeated dose toxicity studies with rodents, and found that the tools Journal of Environmental Biology January 2011 Comparison of statistical tools used in Japan with other countries Japan (1)Japan (3)France (2)U.S.A. (5)Finland (2)Turkey (1)Japan (6)Japan (12)Japan (14)Japan (15)Japan (9)Japan (4)Japan (13)Japan (2)Algeria (1)China (2)Egypt (2)Hungary (1)Israel (2)Italy (1)Jordan (2)Malaysia (2)Netherlands (2)Nigeria (5)Pakistan (2)Philippines (1)Poland (1)Saudi Arabia (1)Slovakia (3)South Africa (2)Switzerland (1)United Kingdom (1)Argentina (1)Belgium (2)Brazil (1)Chili (1)Chili (3)China (1)China (3)Mexico (3)Spain (2)Thailand (4)Turkey (2)United Kingdom (2)Algentina (2)Cameroon (1)China (4)Cuba (3)Denmark (2)Finland (1)France (1)Greece (2)India (2)India (3)Nigeria (2)Sweden and Netherlands (2)Cuba (2)Israel (3)Mexico (1)Slovakia (1)Denmark (1)Jordan (1)Iran (2)Sweden and Netherlands (1)Japan (5)Japan (8)Japan (11)Netherlands (1)Japan (7)Cameroon (2)Pakistan (1)Russia (2)Switzerland (2)Thailand (2)Turkey (3)U.S.A. (1)U.S.A. (2)U.S.A. (4)France (3)Algeria (2)Canada (2)India (1)India (4)Japan (10)Republic of Slovenia (1)Malaysia (1)Belgium (1)Israel (1)Czech Republic (1)Brazil (2)Czech Republic (2)Egypt (1)India (5)Italy (2)Portugal (1)Saudi Arabia (2)Germany (1)Canada (1)Iran (1)Poland (4)Chili (2)Poland (2)Poland (3)Germany (2)Korea (3)Portugal (2)Spain (4)Spain (3)Nigeria (3)Nigeria (4)Thailand (1)Thailand (3)Poland (5)Poland (6)Greece (1)Korea (2)U.S.A. (3)Mexico (2)Hungary (2)Cuba (1)Spain (1)Korea (1)Denmark (3)South Africa (1) Fig. 1: Classification of statistical analysis methods by cluster analysis. Note: 11 studies in cluster 1 (red), 2 studies in cluster 2 (orange), 109 studies in cluster 3(green) and 5 studies in cluster 4 (blue) Journal of Environmental Biology January 2011 Kobayashi et al.
used were not similar. For example, to analyse the data obtained tool used for post hoc comparison was not mentioned in 14 studies from repeated dose toxicity studies with rodents, Scheffé's multiple range parametric and non-parametric and Dunnett type (joint type The number of animals in the group can greatly influence Dunnett) tests are commonly used in Japan (Sakaki et al., 2000), but outcome of the statistical analysis of the study. It is also common to in the other countries use of these tools is not so common. However, encounter mortality in repeated dose toxicity studies, which results in statistical techniques used for testing the data obtained from these difference in number of animals among the groups. In such situation, studies for homogeneity of variance and inter-group comparisons the selected statistical tool may have low power for detecting a do not differ much between Japan and the other countries. In most of significant difference, hence cannot bring out biological y relevant the countries investigated, including Japan, the data are not tested information. Hence, the number of animals to be used in a group in for normality. In Japan, the analysis is usually carried out as per a repeated dose toxicity studies may be decided taking into consideration decision tree (Hamada et al., 1998; Kobayashi, 2000; Kobayashi et of the death that could occur in such studies. Bartlett's test is a very sensitive test for testing homogeneity of variance of the data and was The data of the repeated dose toxicity studies with rodents used in most of the countries investigated. A slight heterogeneity in for the present investigation are from 45 countries and were obtained variance of the data in one group may result in heterogeneity in from internet.
variance in the data of al the groups by Bartlett's test, compel ing thedata to be subjected to a less sensitive non-parametric test. Therefore, Investigational materials and analytical method: Statistical for testing homogeneity of variance, Levene's test, which has low methods used in the 45 countries to analyse the data obtained from sensitivity (Kobayashi et al., 1999) may be more appropriate than repeated dose toxicity studies with rodents are given in Table 1.
the Bartlett's test. Present investigation reveals that the data were Based on the statistical tools used, these studies were grouped in 4 examined only in 6 studies for both normality and homogeneity of clusters as given in Table 2 and were subjected to cluster analysis variance. Ideal y, the data may be examined for both normality and (SAS JMP, Ver. 5, USA). For the cluster analysis, an input of ‘0' was homogeneity of variance (Kobayashi et al., 2008). We suggest given, when a statistical tool was not used and ‘1' was given, when Levene's test for testing homogeneity of variance of the data. If the it was used.
homogeneity of the variance of the groups are not statistical y different,we recommend Dunnett's test, and Steel's test, if it is different.
Results and Discussion The classification of statistical tools used in the 45 countries for analyzing data obtained from repeated dose toxicity studies with OECD: Organization for economic co-operation and development. OECD rodents by cluster analysis is given in Fig. 1. As per the analysis, 11 guideline for the testing of chemicals. Repeated Dose 28-day Oral studies are grouped in cluster 1, 2 in cluster 2, 109 in cluster 3 and Toxicity Study in Rodents, No. 407. OECD, France (1995).
6 studies are grouped in cluster 4. The detection power of statistical EPA: Environmental Protection Agency, USA. Health Effects Test Guidelines, OPPTS 870.3050. Repeated Dose 28–Day Oral Toxicity Study in tools grouped in cluster 1 for finding significant difference among the Rodents. US EPA, Washington, DC (2000).
groups is extremely low. If the variance of the groups is unequal, FDA: Food and Dug Administration, USA. Toxicological Principles for the using the statistical tools of this cluster may not show a significant Safety Assessment of Food Ingredients. Redbook 2000. General difference in the low dose group. The statistical tools of cluster 2 is Guidelines for Designing and Conducting Toxicity Studies. US FDA,Rockville, MD (2003).
close to cluster 1, hence the detection power of this cluster is similar Sakaki, H., S. Igarashi, T. Ikeda, K. Imamizo, T. Omichi, M. Kadota, T.
to that of cluster 1. If the number of animals is different in the groups, Kawaguchi, T. Takizawa, O. Tsukamoto, K. Terai, K. Tozuka, J.
which is usual y seen in repeated dose toxicity studies, the detection Hirata, J. Handa, H. Mizuma, M. Murakami, M. Yamada and H. Yokouchi: power of the statistical tools of this cluster for finding a significant Statistical method appropriate for general toxicological studies in rats.
J. Toxicol. Sci., 25, 71-98 (2000).
difference is further decreased. The statistical tools of cluster 3, which Hamada, C.K., M. Matsumoto, M. Nomura and I. Yoshimura: Three-type has high detection power, is commonly used in most of the countries.
algorithm for statistical analysis in chronic toxicity studies. J. Toxicol.
In cluster 4, statistical tools having high detection power were used to Sci., 23, 173-181 (1998).
examine both homogeneity of variance and normality.
Kobayashi, K.: Trends of the decision tree for selecting hypothesis-testing procedures for the quantitative data obtained in the toxicological bioassay Seven studies from Japan are grouped in cluster 1 of 11 of the rodents in Japan. J. Environ. Biol., 21, 1-9 (2000).
Kobayashi, K., M. Kanamori, K. Ohori and H. Takeuchi: A new decision tree analytical tools, 2 are grouped in cluster 2 of 2 analytical tools and 6 method for statistical analysis of quantitative data obtained in toxicity are grouped in cluster 3 of 109 analytical tools. No study from Japan studies on rodent. San Ei Shi, 42, 125-129 (2000).
is placed in cluster 4.
Katsumi, K., S. Kitajima, D. Miura, K. Inoue, K. Ohori, H. Takeuchi and K.
Takasaki: Characteristics of quantitative data obtained in toxicity rodents Bartlett's test was used to examine homogeneity of variance - The necessity of Bartlett's test for homogeneity of variance to introduce in studies conducted in most of the countries. However, 6 studies a rank test. J. Environ. Biol., 20, 37-48 (1999).
Kobayashi, K., K.S. Pillai, M. Suzuki and J. Wang: Do we need to used Levene's test to examine homogeneity of variance, which has examine the quantitative data obtained from toxicity studies for less power compared to Bartlett's test. Shapiro-Wilks and Kolmogorov- both normality and homogeneity of variance. J. Environ. Biol., 29, Smirnov tests were used in two studies each. Interestingly, statistical 47-52 (2008).
Journal of Environmental Biology January 2011

Source: http://www10.plala.or.jp/biostatistics/2-54.pdf

2012-11_manz-technology.indd

MANZ CORE COMPETENCIES EFFICIENT SYSTEM SOLUTIONS FOR VARIOUS INDUSTRIES Process machines and automation systems by Manz offer many advantages for customers in various industries. They are characterized by maximum efficiency and reliability. Maximum process quality and, at the same time, low cost of ownership are evidence of the high level of development and well-designed system solutions.

Public, products and employers liability

Level 3, 31 Ventnor Ave West Perth PO Box 1141 West Perth WA 6872 AUS (08) 9420 7900 New Business Questionnaire INT +61 8 9420 7900 W edgeunderwriting.com.au PUBLIC LIABILITY PRODUCTS LIABILITY IMPORTANT NOTICES For your protection under legislation, we are required to inform you of your duty of disclosure and draw your attention to the following important information. DUTY OF DISCLOSURE The Insurance Contracts Act 1984 requires insurance companies to provide certain information to people intending to insure with them. The information concerns the duty of disclosure of an intending Insured and the effect of particular types of clauses in a proposed insurance policy. Where an Insurance Broker is involved in the transaction, the information is to be provided by the broker. The Insurance (Agents and Brokers) Act 1984 also requires us to inform you about some other matters where they are relevant to particular policies. WHAT INFORMATION DOES AN INSURANCE COMPANY/BROKER HAVE TO GIVE YOU? In general terms, the kind of information which an insurance company/broker must give you is as follows: YOUR DUTY OF DISCLOSURE – CONTRACTS OF GENERAL INSURANCE SUBJECT TO INSURANCE CONTRACTS ACT Before you enter into a contract of general insurance with an Insurer, you have a duty, under the Insurance Contracts Act 1984, to disclose to the Insurer every matter that you know, or could reasonably be expected to know, that is relevant to the Insurer's decision whether to accept the risk of the insurance, and if so on what terms. You have the same duty to disclose those matters to the Insurer before you renew, extend, vary or reinstate a contract of general insurance. Your duty, however, does not require disclosure of matter: 