Analysis of correlated data in human biomonitoring studies. The case of high sister chromatid exchange frequency cells

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. Copyright (C) 1999 Elsevier Science B.V.