Bonassi S, Fontana V, Ceppi M, Barale R, Biggeri A
Department of Environmental Epidemiology and Biostatistics, National Cancer Institute, Largo Rosanna Benzi 10, 16132, Genoa, Italy.
Mutat Res. 1999 Jan 2;438(1):13-21. doi: 10.1016/s1383-5718(98)00153-3.
Sister chromatid exchange (SCE) analysis in peripheral blood lymphocytes is a well established technique that aims to evaluate human exposure to toxic agents. The individual mean value of SCE per cell had been the only recommended index to measure the extent of this cytogenetic damage until the early 1980's, when the concept of high frequency cells (HFC) was introduced to increase the sensitivity of the assay. All statistical analyses proposed thus far to handle these data are based on measures which refer to the individual mean values and not to the single cell. Although this approach allows the use of simple statistical methods, part of the information provided by the distribution of SCE per single cell within the individual is lost. Using the appropriate methods developed for the analysis of correlated data, it is possible to exploit all the available information. In particular, the use of random-effects models seems to be very promising for the analysis of clustered binary data such as HFC. Logistic normal random-effects models, which allow modelling of the correlation among cells within individuals, have been applied to data from a large study population to highlight the advantages of using this methodology in human biomonitoring studies. The inclusion of random-effects terms in a regression model could explain a significant amount of variability, and accordingly change point and/or interval estimates of the corresponding coefficients. Examples of coefficients that change across different regression models and their interpretation are discussed in detail. One model that seems particularly appropriate is the random intercepts and random slopes model.
外周血淋巴细胞姐妹染色单体交换（SCE）分析是一种成熟的技术，旨在评估人体对有毒物质的暴露情况。直到20世纪80年代初，每个细胞的SCE个体均值一直是衡量这种细胞遗传损伤程度的唯一推荐指标，当时引入了高频细胞（HFC）的概念以提高检测的灵敏度。迄今为止提出的所有处理这些数据的统计分析都是基于涉及个体均值而非单个细胞的测量方法。尽管这种方法允许使用简单的统计方法，但个体内单个细胞SCE分布所提供的部分信息会丢失。使用为相关数据分析开发的适当方法，可以利用所有可用信息。特别是，随机效应模型的使用对于分析如HFC这样的聚类二元数据似乎非常有前景。逻辑正态随机效应模型允许对个体内细胞间的相关性进行建模，已应用于来自大量研究人群的数据，以突出在人体生物监测研究中使用这种方法的优势。在回归模型中纳入随机效应项可以解释大量的变异性，并相应地改变相应系数的变化点和/或区间估计。详细讨论了在不同回归模型中变化的系数示例及其解释。一种似乎特别合适的模型是随机截距和随机斜率模型。
