The Extended Statistical Analysis of Toxicity Tests Using Standardised Effect Sizes (SESs): A Comparison of Nine Published Papers

Abstract

The safety of chemicals, drugs, novel foods and genetically modified crops is often tested using repeat-dose sub-acute toxicity tests in rats or mice. It is important to avoid misinterpretations of the results as these tests are used to help determine safe exposure levels in humans. Treated and control groups are compared for a range of haematological, biochemical and other biomarkers which may indicate tissue damage or other adverse effects. However, the statistical analysis and presentation of such data poses problems due to the large number of statistical tests which are involved. Often, it is not clear whether a “statistically significant” effect is real or a false positive (type I error) due to sampling variation. The author's conclusions appear to be reached somewhat subjectively by the pattern of statistical significances, discounting those which they judge to be type I errors and ignoring any biomarker where the p-value is greater than p = 0.05. However, by using standardised effect sizes (SESs) a range of graphical methods and an over-all assessment of the mean absolute response can be made. The approach is an extension, not a replacement of existing methods. It is intended to assist toxicologists and regulators in the interpretation of the results. Here, the SES analysis has been applied to data from nine published sub-acute toxicity tests in order to compare the findings with those of the author's. Line plots, box plots and bar plots show the pattern of response. Dose-response relationships are easily seen. A “bootstrap” test compares the mean absolute differences across dose groups. In four out of seven papers where the no observed adverse effect level (NOAEL) was estimated by the authors, it was set too high according to the bootstrap test, suggesting that possible toxicity is under-estimated.

Comparative Study of Multitask Toxicity Modeling on a Broad Chemical Space

“…Because it is involved in many organism’s systems and metabolic pathways, toxicity can not be easily modeled solely by calculation. Moreover, the methodology of the experiments that measure toxicity, and the statistical analysis of the data obtained is under criticism. , …”

“…Moreover, the methodology of the experiments that measure toxicity, and the statistical analysis of the data obtained is under criticism. 3,4 Toxicity estimation can be performed in two main ways: in vivo using rodent models or clinical trials data and in vitro using cell-based bioassays. The former approach allows for the estimation of the toxic effect, at the organism level, producing comprehensive results, and is widely used in preclinical tests.…”

Specific Reprogramming of Alpha Cells to Insulin-Producing Cells by Short Glucagon Promoter-Driven Pdx1 and MafA

“…Sample size was determined according to the published literature (30). Immunohistochemistry quanti cation was performed on at least 5 sections that were 100 µm apart.…”

“…Sample size was determined according to the published literature (30) samples from each condition. All data were statistically analyzed by 2-tailed Student's t-test.…”

Specific Reprogramming of Alpha Cells to Insulin-Producing Cells by Short Glucagon Promoter-Driven Pdx1 and MafA

“…Sample size was determined according to the published literature (32). Immunohistochemistry quanti cation was performed on at least 5 sections that were 100 μm apart.…”