Limit of detection values in data analysis: Do they matter?

Abstract

Data sets containing values below the limit of detection (LOD) are known as 'censored data sets'. Such data sets are encountered regularly in most fields of environmental contaminant research. The current norm within environmental radioactivity research is to use substitution methods when analysing data sets that include values below the LOD, commonly replacing each LOD value with a value equal to half the LOD (LOD/2). However, this approach has no statistical basis and has implications when summarising or comparing data sets because it can lead to underestimates or overestimates of both the mean and the standard deviation. To remove the need to apply substitution methods, over the last four decades other fields of environmental science have been adopting statistical techniques developed for medical research applications. Despite the long history of applying these techniques in other fields and two recent environmental radioactivity publications that have used survival analysis techniques, there still seems to be reluctance within the environmental radioactivity research community to adopt these 'new' methods. This paper introduces the statistical techniques that can be used in place of LOD substitution, presents some guidance on the applicability of these techniques for different levels of data censoring and provides some examples of the use of these methods in various contexts. It is hoped the present paper will contribute to the evidence-base supporting the use of survival analysis within the field of environmental radioactivity research and go some way to changing the current norm of substitution using LOD/2.