Combining expert ratings and exposure measurements: a random effect paradigm.

The aim of this paper is to present a paradigm for combining ordinal expert ratings with exposure measurements while accounting for a between-worker effect when estimating exposure group (EG)-specific means for epidemiological purposes. Expert judgement is used to classify the EGs into a limited number of exposure levels independently of the exposure measurements. The mean exposure of each EG is considered to be a random deviate from a central exposure rating-specific value. Combining this approach with the standard between-worker random effect model, we obtain a nested two-way model. Using Gibbs sampling, we can fit such models incorporating prior information on components of variance and modelling options to the rating-specific means. An approximate formula is presented estimating the mean exposure of each EG as a function of the geometric mean of the measurements in this EG, between and within EG standard deviations and the overall geometric mean, thus borrowing information from other EGs. We apply this paradigm to an actual data set of dust exposure measurements in a steel producing factory. Some EG-specific means are quite different from the estimates including the ratings. Rating-specific means could be estimated under different hypotheses. It is argued that when setting up an expert rating of exposures it is best done independently of existing exposure measurements. The present model is then a convenient framework in which to combine the two sources of information.