Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Test for Homogeneity

Test for Homogeneity

The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can be stated as...

Friedman Two-way Analysis of Variance by Ranks

Friedman Two-way Analysis of Variance by Ranks

Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test

In parametric statistics, two fundamental tests stand out for their utility and wide application: the Student's t-test and goodness-of-fit tests. These tests provide researchers with a robust method for drawing insights from data, testing hypotheses, and making informed decisions based on their findings.
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with data...

Ordinal Level of Measurement

Ordinal Level of Measurement

The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks in the...

Expected Frequencies in Goodness-of-Fit Tests

Expected Frequencies in Goodness-of-Fit Tests

A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).

Wald-Wolfowitz Runs Test I

Wald-Wolfowitz Runs Test I

The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...

You might also read

Related Articles

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

Sort by

Same author

Diagnosis concealment is prevalent in MS, and associated with diagnosis experience.

Multiple sclerosis and related disorders·2022

Same author

High chromosomal mobility of rDNA clusters in holocentric chromosomes of Triatominae, vectors of Chagas disease (Hemiptera-Reduviidae).

Medical and veterinary entomology·2021

Same author

EAN guideline on palliative care of people with severe, progressive multiple sclerosis.

European journal of neurology·2020

Same author

Polygenic liability for schizophrenia and childhood adversity influences daily-life emotion dysregulation and psychosis proneness.

Acta psychiatrica Scandinavica·2020

Same author

Italian consensus on treatment of spasticity in multiple sclerosis.

European journal of neurology·2019

Same author

Trypanosoma cruzi diversity in infected dogs from areas of the north coast of Chile.

Veterinary parasitology, regional studies and reports·2019

Search research articles

Related Experiment Video

Updated: Jul 4, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Testing marginal homogeneity against stochastic order in multivariate ordinal data.

B Klingenberg¹, A Solari, L Salmaso

¹Department of Mathematics and Statistics, Williams College, Williamstown, Massachusetts 01267, USA. bklingen@williams.edu

|May 31, 2008

Summary

This study introduces a new statistical method for analyzing multiple, correlated ordinal endpoints in toxicity and safety assessments. The approach uses permutation tests to reliably detect treatment effects, even with sparse data.

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Related Experiment Videos

Last Updated: Jul 4, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Area of Science:

Biostatistics
Pharmacology
Toxicology

Background:

Ordinal endpoints are crucial for assessing treatment effects in safety and toxicity studies.
Correlated ordinal data often present challenges due to sparsity and imbalance, complicating traditional statistical analysis.

Purpose of the Study:

To develop a robust statistical methodology for analyzing multiple, correlated ordinal endpoints.
To provide a more reliable approach for evaluating treatment effects in the presence of complex data structures.

Main Methods:

Utilized stochastic order and marginal inhomogeneity to detect treatment effects under weaker assumptions.
Employed permutation or bootstrap distributions for significance testing, naturally accounting for endpoint correlations.
Developed subgroup analyses for enhanced power and applied closed testing procedures for multiplicity adjustments.

Main Results:

Demonstrated a theorem connecting marginal homogeneity with exchangeability under permutation testing.
Successfully applied the methodology to a dataset of 25 correlated ordinal endpoints in chemical compound toxicity evaluation.
Showcased the power of subgroup analysis over individual endpoint analysis.

Conclusions:

The proposed permutation-based methodology offers a powerful and flexible alternative to traditional modeling for correlated ordinal data.
Subgroup analysis provides a more sensitive approach to detecting treatment effects in toxicity and safety evaluations.
The method effectively handles sparse and imbalanced contingency tables common in such assessments.