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

Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

608
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 0s. In...
608
z Scores and Area Under the Curve01:17

z Scores and Area Under the Curve

19.9K
z scores are the standardized values obtained after converting a normal distribution into a standard normal distribution. A z score is measured in units of the standard deviation. The z score tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a z score of...
19.9K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

4.3K
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...
4.3K
Review and Preview01:10

Review and Preview

8.7K
In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
Percentiles are a type of fractile that partition data into...
8.7K
Introduction to z Scores01:05

Introduction to z Scores

1.5K
A z score (or standardized value) is measured in units of the standard deviation. It indicates how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
z scores...
1.5K
Introduction to z Scores01:06

Introduction to z Scores

11.6K
A z score (or standardized value) is measured in units of the standard deviation. It tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
z scores...
11.6K

You might also read

Related Articles

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

Sort by
Same author

Assessment of Immunogenicity of Multi-Epitope DNA Vaccine Encoding CDPK3, ROP22 & MIC8 of <i>Toxoplasma gondii</i> Adjuvanted with IL-12 against Acute and Chronic Toxoplasmosis in BALB/c Mice.

Iranian journal of parasitology·2026
Same author

A global systematic review and meta-analysis on preterm birth and air pollution exposure.

Public health·2026
Same author

Group-Based Advance Care Planning for Parkinson's Spectrum Disorders: A Retrospective Evaluation of an Integrated Outpatient Model.

The American journal of hospice & palliative care·2026
Same author

Unravelling the use and sequence of regulated learning in online collaborative learning: A pilot study.

Medical teacher·2025
Same author

Cryopreservation as an Alternative Approach for Applying Photopheresis Products to Treat Graft-Versus-Host Disease (GVHD) Among Pediatric Patients in Developing Countries.

Pediatric transplantation·2025
Same author

Investigating the simultaneous effect of longitudinal biomarkers on long-term kidney transplant failure in Iranian kidney transplant patients: a multivariate joint model.

BMC nephrology·2025

Related Experiment Video

Updated: Mar 10, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

11.0K

Analyzing Gensini Score as a Semi-Continuous Outcome.

Homa Kashani1, Hojjat Zeraati1, Kazem Mohammad1

  • 1Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.

The Journal of Tehran Heart Center
|December 9, 2016
PubMed
Summary

Statistical models for semi-continuous outcomes like the Gensini score are crucial for analyzing coronary artery disease (CAD). The two-part model effectively assessed Factor V Leiden

Keywords:
Coronary angiographyCoronary artery diseaseData interpretation, statistical

More Related Videos

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

2.7K
Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays
14:06

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Published on: November 12, 2012

47.1K

Related Experiment Videos

Last Updated: Mar 10, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

11.0K
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

2.7K
Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays
14:06

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Published on: November 12, 2012

47.1K

Area of Science:

  • Statistics
  • Cardiology
  • Genetics

Background:

  • Semi-continuous outcomes, characterized by a large proportion of zero values, are common in medical research, exemplified by the Gensini score for coronary artery disease (CAD) severity.
  • Accurate statistical modeling is essential for understanding the relationship between genetic factors, like Factor V Leiden, and CAD presence and severity.
  • Traditional statistical methods may not adequately handle the complexities of semi-continuous data, potentially leading to information loss or biased results.

Purpose of the Study:

  • To evaluate two statistical approaches, the generalized ordinal threshold model and the two-part model, for analyzing semi-continuous data.
  • To assess the association between Factor V Leiden and both the presence and severity of coronary artery disease (CAD) using these models.
  • To compare the performance and interpretability of the two models in the context of semi-continuous outcomes.

Main Methods:

  • Data from 1594 patients with suspected CAD were analyzed, including demographic, clinical, and Gensini score data.
  • The generalized ordinal threshold model and the two-part model were applied to assess the association between Factor V Leiden and CAD.
  • Statistical significance and effect sizes were determined, with particular attention to the proportional odds assumption for the ordinal model.

Main Results:

  • Neither model indicated a significant association between Factor V Leiden and the presence of CAD.
  • The two-part model revealed that Factor V Leiden was associated with increased CAD severity, with higher Gensini scores in heterozygotes and homozygotes compared to wild genotypes.
  • The proportional odds assumption was violated for the generalized ordinal threshold model, although a trend towards more severe CAD was observed across Gensini score categories.

Conclusions:

  • Categorizing semi-continuous outcomes like the Gensini score can lead to information loss and complicate interpretation.
  • Violation of the proportional odds assumption in models like the generalized ordinal threshold model poses challenges for clinical decision-making.
  • The two-part model offers a more straightforward and interpretable approach for analyzing semi-continuous outcomes, warranting greater consideration in research.