Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

6.5K
A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
6.5K
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

300
The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and...
300
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.8K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.8K
Confidence Intervals01:21

Confidence Intervals

7.1K
An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
7.1K
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

266
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.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
266
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

6.3K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
6.3K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Indirect methods for the verification of reference intervals in laboratory medicine.

Critical reviews in clinical laboratory sciences·2026
Same author

Exercise-induced changes in hemostasis markers in marathon runners: effects of enzyme supplementation and determinants.

Frontiers in physiology·2026
Same author

Integrative multi-omics analysis of growth plate regulation underlying body size in miniature pigs.

Communications biology·2026
Same author

Continuous reference intervals for high-sensitivity cardiac troponin T in children: a closed-form approach with zlog transformation.

Clinical chemistry and laboratory medicine·2026
Same author

Genetic screening of children for familial hypercholesterolaemia: the VRONI study.

European heart journal·2026
Same author

Impact of lipoprotein(a) on the durability of aortic bioprosthetic valves.

Clinical research in cardiology : official journal of the German Cardiac Society·2026

Related Experiment Video

Updated: Aug 23, 2025

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

15.3K

A zlog-based algorithm and tool for plausibility checks of reference intervals.

Sandra Klawitter1,2, Georg Hoffmann1,3, Stefan Holdenrieder3

  • 1Trillium GmbH Medizinischer Fachverlag, Grafrath, Germany.

Clinical Chemistry and Laboratory Medicine
|November 2, 2022
PubMed
Summary

A new R tool uses z-score standardization to automatically check laboratory reference limits for critical age-related jumps. This algorithm aids in ensuring the accuracy of pediatric reference intervals, improving lab data quality.

Keywords:
age-dependent reference intervalsquality managementzlog value

More Related Videos

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

2.6K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.2K

Related Experiment Videos

Last Updated: Aug 23, 2025

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

15.3K
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

2.6K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.2K

Area of Science:

  • Clinical Laboratory Science
  • Bioinformatics
  • Pediatric Reference Intervals

Background:

  • Laboratory information systems manage extensive age- and sex-stratified reference limits.
  • Manual plausibility checks of these limits are often infeasible.
  • Automated tools are needed to ensure the integrity of reference intervals.

Purpose of the Study:

  • To develop a simple algorithm for plausibility checks of laboratory reference limits.
  • To create an open-source R tool (Shiny application) for this purpose.
  • To validate the algorithm using pediatric reference intervals from the CALIPER initiative.

Main Methods:

  • Utilized z-score standardization (zlog) based on reference limits, not raw data.
  • Developed an R Shiny application to identify, tabulate, and plot zlog values.
  • Applied the tool to analyze reference intervals across pediatric age groups.

Main Results:

  • The algorithm detected significant, rapid changes in reference intervals between neonatal and pubertal stages.
  • 15.1% of reference limits (29/192) showed zlog values exceeding 5, indicating potential issues.
  • The tool visualizes these critical jumps in a colored table and plots.

Conclusions:

  • Age-partitioned reference intervals are standard in laboratory diagnostics.
  • Algorithmic approaches like the zlog method are essential for large-scale plausibility testing.
  • The developed Shiny application provides a valuable tool for ensuring the reliability of laboratory reference limits.