Data Representativeness: Issues and Solutions

In its control programmes on maximum residue level compliance and exposure assessments, EFSA requires theparticipating countries to submit results, from specific numbers of food item samples, analyzed in the countries.These data are used to obtain estimates such as the proportion of samples exceeding the maximum residue limits,and the mean and maximum residue concentration per food item to assess exposure. An important considerationis the design and analysis of the programmes. In this report, we combine elements of survey samplingmethodology, and statistical modeling, as a benchmark framework for the programmes, starting from thetranslation of research questions into statistical problems, to the statistical analysis and interpretation. Particularfocus is placed on the issues that could affect the representativeness of the data, and remedial procedures areproposed. For example, in the absence of information on the sampling design, a sensitivity analysis, across arange of designs, is proposed. On the other hand, weighted generalized linear mixed models, and generalizedlinear mixed models combining both conjugate and normal random effects, are proposed, to address selectionbias. Likelihood-based analysis methods are also proposed to address missing and censored data problems.Suggestions for improvements in the design and analysis of the programmes are also identified and discussed.For instance, incorporation of stratified sampling methodology, in determining both the total number, and theallocation of samples to the participating countries, is proposed. All through the report, statistical analysismodels which properly take into account the hierarchical (and thus correlated) structure in which the data arecollected are proposed.