Statistical Guidance for Reviewers of Toxicologic Pathology

Abstract

Study design, statistical analysis, interpretation of results, and conclusions should be a part of all research papers. Statistics are integral to each of these components and are therefore necessary to evaluate during manuscript peer review. Research published in Toxicological Pathology is often focused on animal studies that may seek to compare defined treatment groups in randomized controlled experiments or focus on the reliability of measurements and diagnostic accuracy of observed lesions from preexisting studies. Reviewers should distinguish scientific research goals that aim to test sufficient effect size differences (i.e., minimizing false positive rates) from common toxicologic goals of detecting a harmful effect (i.e., minimizing false negative rates). This journal comprises a wide range of study designs that require different kinds of statistical assessments. Therefore, statistical methods should be described in enough detail so that the experiment can be repeated by other research groups. The misuse of statistics will impede reproducibility.

Keywords: biostatistics; manuscript peer review; statistical analysis; study design.

Figure 1. Distributions of different data types

The appropriate measure of central tendency for illustrative distributions of (a) numeric and (b) categorical data is indicated in the figure for mean (solid vertical line), median (dashed vertical line) and mode. Mean refers to the average value, median is the middle value and mode is the value that appears the most often in a distribution. Dispersion can be measured according to standard deviation (or standard error), interquartile range and range. Standard deviation is the average deviation of scores from the mean and is useful when describing the variability of measurements. On the other hand, the standard error is the standard deviation of a sampling distribution of the mean and is useful when describing the uncertainty around the mean. The interquartile range (IQR) is the difference between upper and lower quartiles. Range refers to the difference between highest and lowest observed values. The central tendency of continuous data can be represented as the mean, the median or the mode; ordinal data should be described by the median or the mode; and nominal data should only be described by the mode. With symmetric data, mean (± standard deviation or standard error) is usually preferable. However, if the data are skewed or contain influential outliers, then the median and IQR are more suitable. IQR is more informative than range for ordinal data. The dispersion measure for nominal data is the modal percentage, i.e., the percentage of the sample that belongs to the modal category.

Figure 1. Distributions of different data types

The appropriate measure of central tendency for illustrative distributions of (a) numeric and (b) categorical data is indicated in the figure for mean (solid vertical line), median (dashed vertical line) and mode. Mean refers to the average value, median is the middle value and mode is the value that appears the most often in a distribution. Dispersion can be measured according to standard deviation (or standard error), interquartile range and range. Standard deviation is the average deviation of scores from the mean and is useful when describing the variability of measurements. On the other hand, the standard error is the standard deviation of a sampling distribution of the mean and is useful when describing the uncertainty around the mean. The interquartile range (IQR) is the difference between upper and lower quartiles. Range refers to the difference between highest and lowest observed values. The central tendency of continuous data can be represented as the mean, the median or the mode; ordinal data should be described by the median or the mode; and nominal data should only be described by the mode. With symmetric data, mean (± standard deviation or standard error) is usually preferable. However, if the data are skewed or contain influential outliers, then the median and IQR are more suitable. IQR is more informative than range for ordinal data. The dispersion measure for nominal data is the modal percentage, i.e., the percentage of the sample that belongs to the modal category.