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Setting performance goals and evaluating total analytical error for diagnostic assays.
1Krouwer Consulting, 26 Parks Dr., Sherborn, MA 01770, USA. jan.krouwer@attbi.com
Clinical Chemistry
|May 25, 2002
Summary
Total analytical error estimation requires direct methods like distribution-of-differences or simulation, not simple models, to accurately assess laboratory assay quality and minimize risks from outliers.
Area of Science:
- Clinical Chemistry
- Laboratory Medicine
- Analytical Science
Background:
- Total analytical error (TAE) is crucial for assessing laboratory assay quality and setting performance goals.
- Current estimation often uses a simple combination model (bias + 1.65 x imprecision), which may differ from direct methods.
Purpose of the Study:
- To reconcile different approaches for estimating total analytical error.
- To identify limitations of the simple combination model and propose superior estimation methods.
Main Methods:
- A comprehensive literature review was conducted to compare various TAE estimation techniques.
- Simulation methods for TAE estimation were outlined, focusing on error source distribution and goal allocation.
Main Results:
- The simple combination model can underestimate TAE by omitting random interference bias and mischaracterizing errors like drift and outliers.
- Simulation and distribution-of-differences methods provide more accurate TAE estimates.
- Outlier rates, critical for quality assessment, are infrequently reported.
Conclusions:
- Direct estimation of TAE using distribution-of-differences or simulation is recommended.
- A systems engineering approach, allocating TAE goals to error sources, offers cost-effectiveness.
- Including outlier rate data from large studies is vital for robust assay quality evaluation.