Fuzzy goal programming utilizes fuzzy set theory to deal with a level of imprecision in the goal programming model.
The measured level of imprecision of both assays was <12% CV for PMN CD64, monocyte CD64, and monocyte CD163 indices.
However, this method might contain a considerable level of imprecision because of the multiple statistical imputations.
Moreover, the wide confidence intervals in some analyses indicate a high level of imprecision in the observed odds ratios.
Because the degree of imprecision of the proxy variable is unknown, a range of imprecision levels were entered to explore the effect on the mercury regression coefficients.
Existing approaches in the literature indicate that, computation of different data forms are closely linked with the level of imprecision, which the data already have.
First, it confirmed that a high level of analytical accuracy was possible, with virtually no bias observed at the high QC coupled to a low level of imprecision.
If data are found to fall outside the accepted DQO limits, then corrective actions (e.g., re-analyse suspect samples, re-sampling and reanalysis, accept data with an acknowledged level of bias and imprecision, discard data, etc).
Numbers, words and perceptions are the forms of data that has varying levels of imprecision.
The analytical range was 62.5-20000 ng/l with an intra-assay imprecision of 4% and an inter-assay imprecision of 7%.
This analyzer uses enzymatic reagents and colorimetric assays, with an assay imprecision of <4%.
