title: Category-specific uncertainty modeling in clinical laboratory measurement processes.
abstract: BACKGROUND
  A statement of measurement uncertainty describes the quality of a clinical assay analysis result, and uncertainty models of clinical assays can be used to evaluate and optimize laboratory protocols designed to minimize the measurement uncertainty associated with an assay. In this study, we propose a methodology to lend systematic structure to the uncertainty modeling process.
  
  
  METHODS
  Clinical laboratory assays are typically classified based on the chemical reaction involved, and therefore, based on the assay analysis methodology. We use this fact to demonstrate that uncertainty models for assays within the same category are structurally identical in all respects except for the values of certain model parameters. This is accomplished by building uncertainty models for assays belonging to two categories--substrate assays based on optical absorbance analysis of endpoint reactions, and ion selective electrode (ISE) assays based on potentiometric measurements of electromotive force.
  
  
  RESULTS
  Uncertainty models for the substrate assays and the ISE assays are built, and for each category, a general mathematical framework for the uncertainty model is developed. The parameters of the general framework that vary from assay to assay for each category are identified and listed.
  
  
  CONCLUSIONS
  Estimates of measurement uncertainty from the models were compared with estimates of uncertainty from quality control data from the clinical laboratory. We demonstrate that building a general modeling framework for each assay category and plugging in parameter values for each assay is sufficient to generate uncertainty models for an assay within a given category.
authors: Varun
authors: V
authors: Ramamohan
authors: Yuehwern
authors: Y
authors: Yih
authors: James T
authors: JT
authors: Abbott
authors: George G
authors: GG
authors: Klee
mesh_terms: D019411:Clinical Laboratory Techniques; D006801:Humans; D015233:Models, Statistical; D035501:Uncertainty