Verde Pablo E, Geracitano Laura A, Amado Lílian L, Rosa Carlos E, Bianchini Adalto, Monserrat José M
Coordination Centre for Clinical Trials and Department of Social Medicine, Heinrich Heine Universitaet Duesseldorf, Moorenstr. 5, D-40225 Duesseldorf, Germany.
Mutat Res. 2006 Apr 30;604(1-2):71-82. doi: 10.1016/j.mrgentox.2006.01.002. Epub 2006 Mar 15.
A novel approach for statistical analysis of comet assay data (i.e.: tail moment) is proposed, employing public-domain statistical software, the R system. The analytical strategy takes into account that the distribution of comet assay data, like the tail moment, is usually skewed and do not follow a normal distribution. Probability distributions used to model comet assay data included: the Weibull, the exponential, the logistic, the normal, the log normal and log-logistic distribution. In this approach it was also considered that heterogeneity observed among experimental units is a random feature of the comet assay data. This statistical model can be characterized with a location parameter m(ij), a scale parameter r and a between experimental units variability parameter theta. In the logarithmic scale, the parameter m(ij) depends additively on treatment and random effects, as follows: log(m(ij)) = a0 + a1x(ij) + b(i), where exp(a0) represents approximately the mean value of the control group, exp(a1) can be interpreted as the relative risk of damage with respect to the control group, x(ij) is an indicator of experimental group and exp(b(i)) is the individual risk effects assume to follows a Gamma distribution with mean 1 and variance theta. Model selection is based on Akaike's information criteria (AIC). Real data coming from comet analysis of blood samples taken from the flounder Paralichtys orbignyanus (Teleostei: Paralichtyidae) and from samples of cells suspension obtained from the estuarine polychaeta Laeonereis acuta (Nereididae) were employed. This statistical approach showed that the comet assay data should be analyzed under a modeling framework that take into account the important features of these measurements. Model selection and heterogeneity between experimental units play central points in the analysis of these data.
本文提出了一种利用公共领域统计软件R系统对彗星试验数据（即：尾矩）进行统计分析的新方法。该分析策略考虑到彗星试验数据的分布，如尾矩，通常是偏态的，不遵循正态分布。用于模拟彗星试验数据的概率分布包括：威布尔分布、指数分布、逻辑分布、正态分布、对数正态分布和对数逻辑分布。在这种方法中，还考虑到实验单位之间观察到的异质性是彗星试验数据的一个随机特征。这个统计模型可以用一个位置参数m(ij)、一个尺度参数r和一个实验单位间变异参数theta来表征。在对数尺度下，参数m(ij)依赖于处理和随机效应的加性，如下所示：log(m(ij)) = a0 + a1x(ij) + b(i)，其中exp(a0)近似代表对照组的平均值，exp(a1)可解释为相对于对照组的损伤相对风险，x(ij)是实验组的指标，exp(b(i))是个体风险效应，假定其遵循均值为1、方差为theta的伽马分布。模型选择基于赤池信息准则（AIC）。使用了来自比目鱼Paralichtys orbignyanus（硬骨鱼纲：Paralichtyidae）血液样本彗星分析的实际数据，以及来自河口多毛纲Laeonereis acuta（沙蚕科）细胞悬浮液样本的数据。这种统计方法表明，彗星试验数据应在考虑这些测量重要特征的建模框架下进行分析。模型选择和实验单位之间的异质性在这些数据的分析中起着核心作用。
