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Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
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Irregular analytical errors in diagnostic testing - a novel concept.

Michael Vogeser1, Christoph Seger2

  • 1Institute of Laboratory Medicine, University Hospital, LMU Munich, Germany, Marchioninistr. 15, 81377 München, Germany.

Clinical Chemistry and Laboratory Medicine
|September 14, 2017
PubMed
Summary
This summary is machine-generated.

Laboratory quality control currently misses individual sample errors. We introduce "irregular analytical error" to identify inaccuracies in single diagnostic tests, improving patient safety.

Keywords:
analytical phaseanalytical qualitydiagnostic errorslaboratory errorlaboratory medicinemethod biaspatient safetypre-analytical phasereference methodrisk analysistotal testing process

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Area of Science:

  • Clinical Chemistry
  • Laboratory Medicine
  • Analytical Science

Background:

  • Routine laboratory quality control (QC) focuses on batch-level analysis, not individual samples.
  • Current QC methods do not detect errors affecting single diagnostic test results.
  • Individual sample interferences can compromise analyte accuracy.

Purpose of the Study:

  • To introduce and define

Main Methods:

  • Proposed a new term: irregular (individual) analytical error.
  • Defined this error as a deviation from reference measurement procedures exceeding routine assay uncertainty.
  • Suggested application in assays traceable to reference measurement systems.

Main Results:

  • Irregular analytical errors arise from individual sample matrix effects or processing errors.
  • These errors are deviations beyond established measurement uncertainty and bias.
  • Examples include anti-reagent antibodies and incorrect pipetting.

Conclusions:

  • Irregular analytical error provides a framework for understanding individual sample inaccuracies.
  • Recognizing these errors is crucial for improving diagnostic test reliability.
  • A catalog of causes aids in mitigating risks to patient safety.