This week's seminars
Stats4Grads: Analysis of Overdispersion in Gamma-H2AX Data
4 December 2019 13:00 in E101
Count data which exhibit overdispersion are extensive in a wide variety of disciplines, such as public health and environmental science. It is typically assumed that the total (aggregated) number of gamma-H2AX foci (DNA repair proteins) produced in a sample of blood cells is Poisson distributed, whose expected yield (average foci per cell) can be represented by a linear function of the absorbed dose. However, in practice, because of unobserved heterogeneity in the cell population, the standard Poisson assumption of equidispersion will most likely be contravened which will cause the variance of the aggregated foci counts to be larger than their mean. In both whole and partial body exposure this phenomenon is perceptible, unlike in the context of the “gold-standard” dicentric assay in which overdispersion is only linked to partial exposure. For such situations, it is possible that utilising a model that can handle overdispersion such as the quasi-Poisson is more preferable to the standard Poisson.
There are many different possible causes of overdispersion and in any modelling situation a number of these could be involved. For our data, some possibilities include experimental variability (for example, a change of technology used in the scoring of cells) and correlation between individual foci counts (or cells) for which both are not accounted for by a fitted model. We will see that the behaviour of dispersion estimates differ considerably between using aggregated data and the full frequency distribution (raw data). To our knowledge, this phenomena has not been investigated in the literature both within and outside the field of biodosimetry. I will explain through simulation how accounting for dependence between observations can impact on the estimated dispersion.
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