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

F Distribution01:19

F Distribution

3.8K
The F distribution was named after Sir Ronald Fisher, an English statistician. The F statistic is a ratio (a fraction) with two sets of degrees of freedom; one for the numerator and one for the denominator. The F distribution is derived from the Student's t distribution. The values of the F distribution are squares of the corresponding values of the t distribution. One-Way ANOVA expands the t test for comparing more than two groups. The scope of that derivation is beyond the level of this...
3.8K
Identifying Statistically Significant Differences: The F-Test01:14

Identifying Statistically Significant Differences: The F-Test

1.7K
The F-test is used to compare two sample variances to each other or compare the sample variance to the population variance. It is used to decide whether an indeterminate error can explain the difference in their values. The underlying assumptions that allow the use of the F-test include the data set or sets are normally distributed, and the data sets are independent of each other. The test statistic F is calculated by dividing one variance by another. In other words, the square of one standard...
1.7K

You might also read

Related Articles

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

Sort by
Same author

Performance of the European Kidney Function Consortium (EKFC) creatinine-based eGFR equation and other eGFR equations in a north European population. A multicentre study in Norway.

Clinical chemistry and laboratory medicine·2026
Same author

Likelihood ratios of quantitative diagnostic test results.

Scandinavian journal of clinical and laboratory investigation·2026
Same author

Interpreting two test results of one analyte from the same individual using bivariate reference values.

Scandinavian journal of clinical and laboratory investigation·2026
Same author

Divide or subtract: transferrin saturation versus unbound iron binding capacity (UIBC).

Scandinavian journal of clinical and laboratory investigation·2026
Same author

Sample stability of forty-two analytes in plasma or serum pools after one to four repeated -80 °C freeze-thaw cycles.

Scandinavian journal of clinical and laboratory investigation·2025
Same author

Testing MMA<sub>100</sub> - the eGFR-adjusted concentration of methylmalonic acid in plasma.

Scandinavian journal of clinical and laboratory investigation·2025

Related Experiment Video

Updated: Jul 21, 2025

FIBS-enabled Noninvasive Metabolic Profiling
09:16

FIBS-enabled Noninvasive Metabolic Profiling

Published on: February 3, 2014

9.9K

Reference change values of FIB-4.

Arne Åsberg1, Lena Løfblad1, Gunhild Garmo Hov1,2

  • 1Department of Clinical Chemistry, St. Olav's Hospital, Trondheim, Norway.

Scandinavian Journal of Clinical and Laboratory Investigation
|July 28, 2023
PubMed
Summary

Clinicians can now use reference change values (RCVs) for the Fibrosis-4 Index (FIB-4) to interpret serial non-cirrhotic liver fibrosis risk assessments. These RCVs, ranging from 0.72 to 1.43, help evaluate significant changes in FIB-4 scores.

Keywords:
Biological variationfatty liverliver cirrhosisliver fibrosisliver function testspopulation

More Related Videos

Real Time Monitoring of Intracellular Bile Acid Dynamics Using a Genetically Encoded FRET-based Bile Acid Sensor
09:21

Real Time Monitoring of Intracellular Bile Acid Dynamics Using a Genetically Encoded FRET-based Bile Acid Sensor

Published on: January 4, 2016

10.0K
A Novel Nicotinamide Adenine Dinucleotide Correction Method for Intracellular Ca2+ Measurement with Fura-2-Analog in Live Cells
05:58

A Novel Nicotinamide Adenine Dinucleotide Correction Method for Intracellular Ca2+ Measurement with Fura-2-Analog in Live Cells

Published on: September 20, 2019

6.8K

Related Experiment Videos

Last Updated: Jul 21, 2025

FIBS-enabled Noninvasive Metabolic Profiling
09:16

FIBS-enabled Noninvasive Metabolic Profiling

Published on: February 3, 2014

9.9K
Real Time Monitoring of Intracellular Bile Acid Dynamics Using a Genetically Encoded FRET-based Bile Acid Sensor
09:21

Real Time Monitoring of Intracellular Bile Acid Dynamics Using a Genetically Encoded FRET-based Bile Acid Sensor

Published on: January 4, 2016

10.0K
A Novel Nicotinamide Adenine Dinucleotide Correction Method for Intracellular Ca2+ Measurement with Fura-2-Analog in Live Cells
05:58

A Novel Nicotinamide Adenine Dinucleotide Correction Method for Intracellular Ca2+ Measurement with Fura-2-Analog in Live Cells

Published on: September 20, 2019

6.8K

Area of Science:

  • Clinical Chemistry
  • Hepatology
  • Biostatistics

Background:

  • Comparing serial analytical results aids clinical decision-making.
  • Reference Change Values (RCVs) are crucial for interpreting analyte changes.
  • No established RCVs exist for the Fibrosis-4 Index (FIB-4) in non-cirrhotic individuals.

Purpose of the Study:

  • To estimate and validate Reference Change Values (RCVs) for the Fibrosis-4 Index (FIB-4) in adults.
  • To provide clinicians with a tool for interpreting serial FIB-4 measurements in non-cirrhotic patients.
  • To assess the biological variation of FIB-4 within subjects.

Main Methods:

  • Retrospective analysis of outpatient data from 599 individuals with two FIB-4 measurements.
  • Calculation of FIB-4 using age, AST, ALT, and platelet count.
  • Estimation of RCVs using parametric and non-parametric statistical methods on the ratio of serial FIB-4 values.

Main Results:

  • Estimated RCVs for FIB-4 were 0.72–1.40 (parametric) and 0.72–1.43 (non-parametric).
  • The 5th and 95th percentiles of the ratio were not significantly associated with sex, age, analyte levels, or time between measurements.
  • The within-subject biological variation of FIB-4 was determined to be 13.9%.

Conclusions:

  • Established RCVs for FIB-4 enable better interpretation of serial test results in non-cirrhotic adults.
  • The derived RCVs (0.7–1.4) indicate that a 90% probability of a true change requires a ratio outside this range.
  • These findings support improved monitoring of liver fibrosis risk using FIB-4.