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Related Concept Videos

Prediction Intervals01:03

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
The...
Interpreting R Charts01:22

Interpreting R Charts

R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum values—of a sample...

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Reference intervals for circulating nucleated red blood cell counts of neonates, improved by the refineR algorithm.

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RIbench: A Proposed Benchmark for the Standardized Evaluation of Indirect Methods for Reference Interval Estimation.

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Implementation of a Reference Interferometer for Nanodetection
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Estimation of Reference Intervals from Routine Data Using the refineR Algorithm-A Practical Guide.

Tatjana Ammer1,2, André Schützenmeister2, Christopher M Rank2

  • 1Friedrich-Alexander-Universität Erlangen-Nürnberg, Chair of Medical Informatics, Erlangen, BY, Germany.

The Journal of Applied Laboratory Medicine
|January 7, 2023
PubMed
Summary
This summary is machine-generated.

This study demonstrates the refineR algorithm

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Area of Science:

  • Clinical chemistry
  • Laboratory medicine
  • Biostatistics

Background:

  • Accurate laboratory test interpretation relies on established reference intervals.
  • Direct determination of reference intervals faces practical and ethical challenges.
  • Indirect methods using routine data offer a viable alternative.

Purpose of the Study:

  • To provide practical guidance on applying the refineR indirect method.
  • To evaluate the performance of refineR using real-world laboratory data.
  • To compare refineR-derived reference intervals with manufacturer-established intervals.

Main Methods:

  • Utilized refineR, a recently published indirect method for reference interval estimation.
  • Applied refineR to prefiltered and cleaned routine patient testing data from ARUP Laboratories.
  • Compared estimated reference intervals with established intervals for 12 analytes.

Main Results:

  • RefineR-estimated reference intervals were comparable to manufacturer-provided intervals for most analytes.
  • Overlapping confidence intervals were observed for both upper and lower reference limits in many cases.
  • Discrepancies were noted for thyroid-stimulating hormone (higher limits) and prealbumin (lower limits).

Conclusions:

  • The refineR algorithm successfully generated comparable reference intervals from real-world data.
  • Practical guidance and code examples facilitate the adoption of indirect methods in labs.
  • Indirect methods like refineR offer a practical solution for establishing reference intervals.